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

Julia A. Yesberg*1, Zöe Hobson1 4, Krisztián Pósch1, Ben Bradford1, Jonathan Jackson2, Arabella Kyprianides1, Reka Solymosi3, Paul Dawson4, Nicole Ramshaw4 and Emily Gilbert4

1 Institute for Global City Policing, Department of Security and Crime Science, University College London, UK 2 Department of Methodology, London School of Economics and Political Science, UK 3 School of Social Science, University of Manchester, UK 4 Mayor’s Office for Policing and Crime, London, UK

*Corresponding author: [email protected]

Abstract In the face of the COVID-19 pandemic, police services around the world were granted unprecedented new powers to enforce social distancing restrictions to help to get the virus under control. Using data from a representative survey of Londoners fielded during the height of the first wave of the pandemic (April – June 2020), we explore the scale of public support for giving police additional powers to enforce the regulations, how support for different powers changed over time, and what factors predicted support. Aside from one lockdown-specific factor, we find that even in the midst of a pandemic, trust, legitimacy and affect were the most important predictors of support for police empowerment.

Keywords: COVID-19; police empowerment; trust; legitimacy; affect; worry about crime

1 Introduction In early 2020 the COVID-19 outbreak plunged much of the world into a public health pandemic. To get to grips with the pandemic and reduce the spread of the virus, many countries introduced emergency powers that significantly curbed civic freedoms and liberties, largely through various forms of lockdown (ICNL, 2020). Citizens around the world—especially in Europe, North America and China—were asked to stay home and avoid contact with others. Naturally, the enforcement of new public health legislation largely fell on the most visible representatives of the justice system: the police. On 26 March 2020 the UK Government enacted new public health legislation that strengthened police enforcement powers in England. Police were granted the power to issue on-the- spot fines to people caught flouting social distancing requirements, to detain people suspected of having COVID-19, and to use force to make people return to their homes (Health Protection Regulations, 2020). The role of the police in enforcing the new public health regulations raised immediate questions about the appropriate boundaries of police power during what was first and foremost a public health crisis. In a country where policing revolves around notions of consent and policing with the public (Bowling, Reiner and Sheptycki, 2019), the concern was that if officers were seen to wield their pandemic powers in unfair, unjust and inappropriate ways, then people may start to question whether the state has the right to impose and enforce these new laws. Our contribution is to shed light on public support for enhanced police powers in the context of a health pandemic, a strong Spring lockdown that went through various phases of relaxation, and throughout all this the need for the police to enforce adherence to public health regulations. At a time when people’s basic freedoms of movement and assembly were being denied, what police powers did Londoners support, did support increase or decrease over time as the national lockdown was eased, and did police activity generate or threaten the trust and legitimacy that they so fundamentally rely upon? Using data from a representative survey of Londoners fielded during the height of the first wave of the pandemic (April – June 2020), we measure support for new powers granted to police (e.g. the ability to issue fines and detain people), and support for surveillance powers that could realistically be used in the future to tackle non-compliance (facial recognition technology and mobile phone tracking). In study one we use interrupted time series analysis to measure change in levels of support for police powers over the height of the first wave of the pandemic. We consider a number of possible ‘points of intervention’, including the multiple easings of the lockdown in England, as well as political ( scandal) and police-related (Black Lives Matter) events. In study two we use structural equation modelling to examine who supports granting police new powers, who does not, and why. Prior UK-based research has looked at the roles that trust, affect and legitimacy play in shaping compliance with rules and laws (Pósch et al., 2020), and support for enhanced powers and new surveillance technologies (Yesberg and Bradford, 2019; Bradford et al., 2020). Drawing on prior work into police empowerment in pre-pandemic times, we explore four sets of factors that may act jointly or separately to motivate support for police powers during the pandemic: (1) worry about crime; (2) trust in the police; (3) police legitimacy; and (4) affective response to the new powers. Each of these potential predictors of support is likely to be correlated, and we examine the relationships between these explanatory variables, as well as with the ultimate response variable: support for police powers. The paper proceeds as follows. First, we outline the UK context and its response to the COVID-19 pandemic. We then set out our conceptual model for understanding public support for police empowerment. Having outlined the methods and data used in the study, we proceed with the results and discussion. We conclude with the idea that even in the midst of a pandemic, support for enhanced police powers in Londoners were predominantly shaped by factors that have been found to be important before COVID-19, and will continue to be important as we move out of the current public health emergency.

Policing COVID-19 in the UK On 23 March 2020 the UK was placed into a nationwide lockdown. People were advised to “stay home, protect the NHS, save lives” (Department of Health & Social Care, 2020), and just a few days later the UK government enacted new public health regulations to strengthen police enforcement

2 powers in England.1 The Health Protection (Coronavirus, Restrictions) (England) Regulations 2020 granted police the power to issue on-the-spot fines to enforce new rules on social distancing, to “direct, remove or detain” anyone suspected of having COVID-19, and to use reasonable force, if necessary, to exercise these powers (Health Protection Regulations, 2020). As a side-effect of the new legislation, people who would not usually come into contact with the police were more likely to do so, with “…the scope of the regulations suddenly [making] potential offenders out of large numbers of otherwise law-abiding people” (Clements and Skidmore, 2020). The police had to weigh up the need to enforce compliance with a desire to not “stray too far from the tradition of policing by consent” (Clements and Skidmore, 2020). Recognising the difficult position the police suddenly found themselves in, the National Police Chiefs’ Council (NPCC) and College of Policing quickly advised officers to adopt a ‘four E’ approach if they believed the rules were not being adhered to): engage with the public; explain the social distancing regulations; encourage people to follow the regulations; and, as a last resort, enforce the law (College of Policing, 2020. What, then, did Londoners think about these new powers? Police were given extraordinary power, most notably the ability to fine people for breaking COVID rules and the ability to detain people to enforce Government coronavirus measures. Yet, polling conducted during the height of the lockdown indicated the ‘four E’ approach had been successful: the majority of the public supported the way the police had handled the lockdown (Desroches and Caluori, 2020). Indeed, 15% of the public wanted the police to take more stringent action to ensure compliance with the rules, with only 6% considering the police to have been too heavy-handed. Our goal in this paper is to provide the first fine-grained empirical account of which powers people supported, how support for different powers seemed to respond to key events (such as the easing of lockdown in phases), how trust, affect and legitimacy ebbed and flowed, and whether the predictors of support were consistent with those found in pre-COVID research. In the next section we review the literature to which we seek to contribute.

Prior research on support for empowering police To empower police means to positively accept a development that grants police more power, including an acceptance of risk that this power may be misused. Prior work has shown that public willingness to empower police is primarily dependent on the extent to which people view the police as trustworthy, think they are a legitimate authority, and feel positive affect towards the new powers (Moule et al. 2019; Bradford et al., 2020; Fox et al., 2020). Also important is whether people are worried about crime. To the extent people believe granting police additional police powers will make ‘fighting crime’ easier and more effective, people who are worried about crime are more likely to accept a development that grants police more power. Indeed, worry about crime has been shown to motivate support for the routine arming of police (Yesberg and Bradford, 2019) and the acceptance of police use of live facial recognition (Bradford et al., 2020). Yet worry about crime seems to be less important than public judgements about those being empowered (i.e. the police). Police empowerment has been more strongly linked to whether people believe the police are trustworthy and legitimate authorities (i.e. that their actions are intrinsically ‘good’ and appropriate, and therefore that they have the authority to govern, which means people consent to various forms of obligation that flow when a power-holder has turned (rightful) power into moral authority). Legitimacy is the normative justification of power that motivates an internalised acceptance of rightful authority among the public (Tyler and Trinkner, 2018; Trinkner, 2019). People who view the police as legitimate feel a moral obligation to follow the rules enforced by the police. They believe police exert their authority in ways that accord with societal expectations about appropriate group conduct (Bradford and Jackson, 2018; Pósch et al., 2020). Crucially, legitimacy grants police the right to enforce the law and dictate appropriate behaviour—their own as well as that of others. Because legitimacy can be withdrawn, renegotiated or attenuated as individuals evaluate police behaviour, rightful power is granted to police on a contingent, not permanent, basis (Hough, 2020). Trust in the police also ebbs and flows according to events and judgements. Based on perceptions of efficacy and competency, and also perceptions that police will treat members of the

1 Wales, Scotland and Northern Ireland have separate regulations. 3 public fairly and respectfully (Perry and Jonathan-Zamir, 2020), the trustworthiness of officers is regularly being re-evaluated and held up against established expectations and norms (Bottoms and Tankebe, 2013; Bradford, Milani and Jackson, 2017). People who trust the police believe they exercise their authority justly. They view them as a capable and effective resource. They are also more willing to accept their vulnerability to the potential misdeeds of police—to place trust— precisely because they expect officers to be willing and able to behave fairly and effectively (Hamm, Trinkner and Carr, 2017). Central here is the idea that trusted and legitimate authorities are enabled and empowered to act on the behalf of those they govern. Previous research has demonstrated that believing the police are a proper and just institution encourages support for actions that fall within existing normative boundaries of behaviour (Bradford, Milani and Jackson, 2017; Gerber and Jackson, 2017; Kyprianides et al., 2020). Research has also shown that trust and legitimacy are important factors for shaping public support for police powers, including the use of modern technologies (Crow et al., 2017; Heen, Lieberman and Miethe, 2017; Sakiyama et al., 2017): people accept new powers because they generally trust police to ‘do the right thing’, to abide by shared norms and values, and to wield their power appropriately. Yesberg and Bradford (2019) found that trust was a heuristic that shaped support for increasing the number of armed officers on the streets of London. Legitimacy also reflects people’s belief that the police monopolise rightful force in society (helping to ‘crowd out’ the belief that private violence is acceptable, see Jackson et al., 2013) and is additionally related to public approval of the use of force when it is proportionate, restrained and justified (Gerber and Jackson, 2017). As Bradford, Milani and Jackson (2017: 615) argue: “Legitimacy is based in an important sense in ‘right behavior,’ but it may also serve to ‘make behavior right’”. Similar results have been found when predicting support for police militarisation. Moule and colleagues (2019) found that greater legitimacy was associated with more support for allowing police to acquire surplus military equipment in the US. In the UK context, support for the routine arming of police was found to be dependent on the extent to which people trust the police (Yesberg and Bradford, 2019). But, importantly, that study found that people’s affective responses to the idea of armed police was by some margin the most important factor in shaping support for this power. People seemed to rely on how they felt about armed officers (whether they would feel safer and comfortable around armed officers) to inform their decision of whether or not to support a policy aimed at arming more police officers. Of course, trust and affect are closely related concepts: people’s affective response to an issue is shaped by the extent to which they trust the relevant actors and are willing to accept vulnerability (Midden and Huijts, 2009). This is especially true when people lack expert understanding, knowledge, or experience of the issue at hand (Merk and Pönitzsch, 2017). Otherwise known as the ‘affect heuristic’ (Slovic et al., 2002), in the absence of knowledge or direct experience, emotional responses (i.e. feeling safer) influence assessments of associated risks and benefits and ultimately willingness to support or oppose police powers. Only one study that we know of has explored predictors of support for police empowerment during the COVID-19 pandemic. In an online study of 1,068 adult US citizens, Nix and colleagues (2020) found that perceived police procedural justice had a positive association with support for social distance policing (i.e. the enforcement of social distancing ordinances). Their findings indicate that the literature on police empowerment in normal times might extend to support for police empowerment during the pandemic. We extend this to include worry about crime, trust, legitimacy and affect as predictors of support for giving police new powers to deal with the pandemic. We also consider the role of trust in government. Given the unprecedented nature of the new regulations, it makes sense to also consider the process by which the police are empowered (i.e. whether people believe the new powers have been legitimately arrived at). In response to the COVID-19 pandemic, the UK government passed emergency new public health regulations under secondary legislation, meaning they were not subject to the same level of parliamentary scrutiny (Cowie, 2020). The extent to which people have trust in the government may therefore play an important role in the acceptance of powers granted to police through this legislation. Finally, one should also consider that support for police powers granted by an emergency legislation is likely to be ephemeral and change over time in accordance with the prevailing situation on the ground (i.e., the pandemic), changes to the pandemic legislation, or other significant events. Looking at data from the height of the first wave of the pandemic in London allows us to provide

4 some indication about whether people changed their minds, and which event(s) (if any) seemed to influence attitudes. We can also start to explain any changes in levels of public support that are attributable to particular events, such as different phases of lockdown, Black Lives Matter, and a key Government advisor seemingly breaking lockdown rules.

The current study In study one we measure the scale of public support for police powers and assess dynamics over time. We use interrupted times series analysis to explore whether support for police powers changed over the height of the first wave of the pandemic. We differentiate between four specific powers related to pandemic policing (issuing fixed penalty notices, detaining people to enforce Government Coronavirus measures, using facial recognition technology to track people, and tracking people’s mobile phones) and a general measure of whether people support giving the police extra powers to deal with Coronavirus. We also test whether specific events that occurred during the study period (e.g. easing of the lockdown, the death of George Floyd) act as ‘tipping points’ that alter the trend in support for police powers. Drawing on prior research (e.g. Yesberg and Bradford, 2019), we test whether the effects of particular events on public support for police powers can be explained, in part, by shifting levels of trust, affect and legitimacy. In study two, and building on the discussion above, we explore predictors of support for pandemic-related police powers. The theoretical model that guides our analysis is presented in Figure 1. This model links legitimacy, trust in government and confidence in the police’s handling of coronavirus back to trust in officers and worry about crime, and allows all variables to predict affect and support for police powers. Legitimacy, institutional trust, and affect also have mediating roles. We test the following six hypotheses:

H1: People who are worried about crime will be more likely to support police empowerment. H2: People who trust the police more will be more likely to support police empowerment. H3: People who grant police legitimacy and who have high levels of institutional trust will be more likely to support police empowerment. H4: People who have more positive affective responses to police powers will be more likely to support police empowerment. H5: Police legitimacy and institutional trust will mediate the association between trust/worry and support for police powers. H6: Affect will mediate the association between trust/worry, police legitimacy/institutional trust and support for police powers.

[Figure 1 here]

We also test the model before and after what we find, in study one, to be the key event: namely, the first easing of lockdown on May 13. This allows us to assess whether there was something specific about those first two months that meant the factors that seem to drive public support in normal times did not pan out quite so simply during that extraordinary time, when people’s freedoms were curtailed to an extent not seen since World War II.

Data and Methods Data Data are drawn from a sample of 3,201 respondents to the Mayor’s Office for Policing and Crime’s (MOPAC) Public Attitude Survey (PAS). The PAS is conducted on a rolling basis and includes a representative sample of residents from across London. Approximately 12,800 Londoners are interviewed face-to-face annually at pre-selected addresses aiming to achieve 400 interviews in each of the 32 London boroughs by the end of each year. Participants in this study were interviewed during one quarter, between April and June 2020. The PAS asks about people’s experiences of crime and anti-social behaviour, and their perceptions of the police. From April 2020, a range of questions related to the COVID-19 crisis were included in the survey. Also from April 2020, to comply with the social distancing regulations, the interviews were conducted via telephone instead of in-person. See Table 1 for sample characteristics.

5 [Table 1 here]

Constructs and measures The PAS contains a range of questions employing Likert-type scales, which are used here to measure the key constructs of interest. Unless otherwise stated, all measures were coded in such a way that higher values indicate more positive evaluations of the construct measured. The items used to measure the key constructs are presented below (see study two results for the process of deriving the latent variables for analysis).

Support for police powers To measure support for police powers to deal with the COVID-19 crisis, participants were asked the following questions: “To what extent do you support or oppose the Service…” (1) getting extra powers to deal with Coronavirus; (2) issuing fixed penalty fines to enforce Government Coronavirus measures; and (3) detaining people to enforce Government Coronavirus measures; (4) using facial recognition technology to track people to ensure they are only out of their homes for a good reason; (5) tracking people’s mobile phones to ensure they are only out of their homes for a good reason. Participants responded on a 5-point scale from strongly oppose to strongly support.

Affective response to police powers One item measured people’s affective response to police powers: “Does the Metropolitan Police Service having extra powers to deal with Coronavirus make you feel more safe, less safe or does it make no difference?”. Responses were coded such that 1=less safe, 2=no difference and 3=more safe. Building on Yesberg and Bradford (2019), who amongst other things measured affect towards armed police by asking people whether they would feel safer if they saw armed officers, we assume that when people say they would feel safer if the police had extra powers to deal with COVID, they are expressing an emotion generated in response to the particular object here (i.e. this is an emotional response attached to the attitude).

Police legitimacy Police legitimacy was measured with one item, representing respondents’ perceived obligation to obey the police: “I feel an obligation to follow police orders”. Participants responded on a 5-point agree/disagree scale.

Trust in Government To measure institutional trust, participants were also asked to rate their level of trust in the UK government: “Central Government is an organisation I can trust”. Participants responded on a 5- point agree/disagree scale.

Confidence in the MPS’s handling of COVID-19 Participants were also asked how confident they were in the London police service to handle the pandemic: “How much confidence do you have in the ability of the Metropolitan Police Service to handle the coronavirus outbreak?”. Participants responded on a 4-point scale from no confidence at all to a lot of confidence.

Trust in police Trust in police was measured along two dimensions: procedural/distributive justice and community engagement. Two items measured procedural/distributive justice: “They would treat you with respect if you had contact with them for any reason” and “The police in your area treat everyone fairly regardless of who they are”. Three items measured community engagement: “They can be relied on to be there when you need them”, “They are dealing with the things that matter to people in this community” and “The police in your area listen to the concerns of local people”. Participants responded to these items a 5-point agree/disagree scale.

6 Worry about crime Worry about crime was measured using two PAS items: “To what extent are you worried about crime in your area” and “To what extent are you worried about anti-social behaviour in your area”. Participants responded on a 4-point scale not at all worried to very worried.

Control variables In our models below, we control for a number of demographic variables, including age, gender, and ethnicity. We also control for the following experiential variables: whether respondents had experienced any crime or incident motivated by or related to the coronavirus outbreak; and whether respondents had been stopped or questioned by the police to check whether they were complying with government COVID-19 measures.

Results The results section is divided into two studies. Study one provides an overview of the scale of support for police powers before using interrupted times series analysis to explore change in support over the study period. Study two derives the latent variables for analysis before testing our conceptual model (Figure 1). We test for direct and indirect statistical effects between our explanatory variables and support for police powers.

Study one Support for police powers We start by presenting respondents’ general level of support for giving police additional powers to deal with the COVID-19 crisis. A clear majority of respondents supported the police getting extra powers to deal with the crisis (83%), including issuing fixed penalty fines to enforce the restrictions (79%). There was slightly less (but still majority) support for giving police the power to detain people (60%). Less than half of respondents supported giving the police the power to use facial recognition technology (48%) and to track people’s mobile phones (38%). It seems, therefore, that the overwhelming majority of Londoners supported giving the police some additional powers to deal with the COVID-19 crisis

Interrupted time series analysis The dataset included the day the interview was conducted, allowing us to track support for police powers over time. We used interrupted time-series analysis to consider when change or changes in the trendline of opinions could have occurred during the study period. A key consideration with interrupted time-series analysis is choosing the point of intervention, which is sometimes referred to as the cut-off or change point. The point of intervention is usually decided a priori and is informed by changes in policy or important events that transpired at that particular moment in time. In our analysis, we considered five points of intervention2:

 13 April: The day after the news broke regarding the prime minister’s key adviser, Dominic Cummings’, apparent breaking of lockdown regulations.  13 May: The first easing of the lockdown in England.  26 May: The day after the death of George Floyd reignites the Black Lives Matter protest movement.  1 June: The second easing of the lockdown in England.  15 June: The third easing of the lockdown in England.

For the Dominic Cummings and George Floyd news stories, we picked the day after the event because we reasoned most people would only have a chance to learn about these events when they had been in

2 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. 7 the headlines for a little while. Conversely, each easing of the lockdown in England was accompanied by a public information campaign by the government in the days leading up to the event. We used interrupted time series analysis as suggested by Linden (2015) and STATA 15 to derive the estimates. Interrupted time series analysis presumes there is one or multiple interventions that meaningfully change the trendline of the variables of interest. Hence, one or multiple pre- and post-intervention trends are compared to each other and the changes to these trends are estimated. 3 In our models, the five items measuring support for particular police powers were the outcome variables. To mitigate the chances that the changes in views were due to sampling variation, we added one explanatory variable in the model, which was a product of the sampling weight and a propensity score which was calculated using the demographic characteristics of each individual (i.e. gender, age, ethnicity). For each day, we took the average of the outcome and explanatory variables, thus generating one observation for each outcome and explanatory variable on each date. The model results are presented in Table 2. We found that support for the use of live facial recognition and mobile tracking remained largely unchanged across the study period, with weak significant pre- and post-intervention trends for the former, and no significant effects for the latter. These findings imply that people in London either did not change their opinions (mobile tracking), or even if they did (live facial recognition), this change could not be attributed to any specific intervention during the study period. For the other three powers, the change in the trendline appeared to happen on 13 May: the first easing of lockdown in England. These changes in the trendline compared to the pre-intervention trend were weak but significant, which is unsurprising given that we were considering one data point for each day (i.e. a relatively small segment of time in the scheme of the pandemic). For general support for police powers, the expected change in the trendline compared to pre-intervention was β=- 0.011 (p<0.05) each day, resulting in a post-intervention trend of similar magnitude (β=-0.011, p<0.05). The second biggest change emerged for support for detaining people, where the average daily change compared to the pre-intervention trend amounted to β=-0.009 (p<0.05), with a post- intervention trend of β=-0.012 (p<0.001). Finally, the estimated daily change compared to pre- intervention trends was -0.004 (p<0.05) for fixed penalty fines, with a β=-0.005 (p<0.05) post intervention trend.

[Table 2 here]

Figure 2 plots the changes in the trendline, providing a visual guide to the effect sizes. The figure shows that (in line with the above findings) the change in the trendline was larger for general support for police powers and support for detaining people: both produced steeper slopes compared to the slope for support for fixed penalty notices. By contrast, the figure for live facial recognition indicates a fairly linear negative trend, while the by-and-large horizontal line for mobile tracking implies that attitudes toward that technology barely changed during the study period.

[Figure 2 here]

Apart from the results regarding the overall trend, our analysis also revealed a couple of significant findings that emerged at the point of the intervention. First, on the day after the Dominic Cummings event, general support for police powers significantly increased (β=0.329, p<0.01). Second, support for fixed penalty notices significantly decreased on the day of the first easing of the lockdown (β=-0.200, p<0.05), as well as the day after George Floyd’s death (β=-0.273, p<0.01). Finally, George Floyd’s death also had a significant negative impact on support for detaining people (β=-0.246, p<0.05). Crucially, however, these effects were short-lived: apart from the first easing of

3 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.

8 the lockdown, the other points of intervention did not appear to change the overall trend in support for police powers.

Trust, legitimacy and affect As a point of comparison, and using the same interrupted time-series approach, we also considered how trends changed in four important explanatory variables: trust in procedural/distributive justice, community engagement, police legitimacy, and affective response. From the four variables, the trendline for police legitimacy was closest to attitudes towards facial recognition, with no statistically significant change over time (Table 3 and Figure 3). The cut-off point for community engagement was on 13 May, with the estimated -0.011 change of the trendline compared to pre-intervention (p<0.01) and a post-intervention trend of similar magnitude (β=-0.010, p<0.01). Likewise, affective response also had its change point on 13 May, with an estimated change of -0.008 (p<0.05). In comparison, for procedural/distributive justice, the modelling appeared to identify two change points: one after the first easing of the lockdown and a second one after George Floyd’s death. After 13 May, the change in trend compared to pre-intervention amounted to -0.017 (p<0.001) with a -0.012 post-intervention trend (p<0.001). In comparison, the estimates for 26 May were slightly weaker, with -0.015 change (p<0.01) and -0.014 post-intervention trend (p<0.01).

[Table 3 here]

The above results indicate that perceptions of police legitimacy did not change during the observed period, whilst attitudes towards community engagement and affective response started to decrease after the easing of the first lockdown. In the case of procedural/distributive justice, the picture was a bit more muddled. We carried out robustness tests (see the Appendix), which implied that the most likely change point for that variable was 11 May and that multiple cut-off points could not be detected. In other words, the 13 May date seems a reasonable choice for this variable as well.

[Figure 3 here]

Study two Next, we consider predictors of support for police powers during the pandemic and test the hypotheses proposed above. We used the package Mplus 7.11 to derive latent variables for analysis and estimate a structural equation model predicting, amongst other things, support for police powers. We used a robust maximum likelihood approach (MLR), which is robust to non-normally distributed data and has been shown to be superior to multiple imputation in handling missing data patterns (Yuan, Yang-Wallentin and Bentler, 2012). We applied sampling weights computed to make the data representative of adults living in London. We used clustered robust standard errors for each day of data collection, where the standard errors were assumed to be correlated within but not between clusters. We derived standardised estimates, so the relative magnitude of the associations (and the contribution made by each variable) could be comparable across the model. Finally, for the estimation of the standard errors of the indirect effects, we took a Bayesian approach with two independent Markov Chain Monte Carlo (MCMC) chains and 100,000 iterations for each chain. Prior literature suggests using Bayesian methods for standard error estimation in mediation analysis to compensate for the non-normal distribution of the indirect effects, thus providing more credible estimates (MacKinnon, Kisbu-sakarya and Gottschall, 2013).

Deriving latent variables for analysis We derived four latent variables for analysis. Item wordings, factor loadings and model fit statistics are shown in the Appendix. First, we derived a latent variable measuring support for police powers using three items: general support for police powers, support for fixed penalty fines, and support for detaining people (we excluded the two surveillance powers due to the lack of change in these variables in study one). This latent variable—support for police powers—forms the dependent variable in subsequent analysis (α=0.80). Second, we derived two latent variables measuring two aspects of trust in police: procedural/ distributive justice (α=0.74) and community engagement (α=0.80). Lastly, we derived a latent variable

9 measuring worry about crime (α=0.80). All other explanatory variables—police legitimacy, trust in government, confidence in the Metropolitan Police Service (MPS) and affect—were measured using a single indicator (see methods section for item wordings). Table 4 presents pairwise correlations between the latent variables and single indicators used in the analysis. As the table shows, support for police powers was most strongly correlated with affect, procedural/distributive justice, and legitimacy.

[Table 4 here]

Predicting support for police powers Figure 4 presents the results from our path analysis. Here, police legitimacy and institutional trust were regressed on procedural/distributive justice, community engagement and worry about crime; affect was regressed on police legitimacy, institutional trust, procedural/distributive justice, community engagement and worry; and support for police powers was regressed on all other variables. We included the first easing of the lockdown in England as a dummy variable because this event was shown in study one to be a likely tipping point for changes in support for police powers. 4 Only paths significant at the 5 per cent level are shown in the figure.

[Figure 4 here]

Starting from the left-hand side of the figure, we found that trust in procedural/distributive justice had significant partial associations with police legitimacy, trust in government and confidence in the MPS to handle the pandemic. Community engagement also had significant partial associations with both trust in government and confidence in the MPS to handle the pandemic. Worry about crime had a weak significant (but negative) partial association with trust in government – those more worried about crime tended to trust the government less. The strongest association was between procedural/distributive justice and police legitimacy (β=.521). Second, police legitimacy and both aspects of institutional trust were positively associated with affect. People who granted police more legitimacy, who trusted the government, and who had confidence in the MPS to handle the pandemic had more positive affective responses to giving police new powers. Third, people worried about crime and who had high levels of trust in procedural/distributive justice also tended to have positive affect toward police powers. Fourth, affect and support for police powers had a strong association (β=.478). Those who reported they would feel safer if police were given extra powers were, unsurprisingly, more supportive of giving police those powers. Lastly, there was a significant negative effect of the easing of the first lockdown on support for police powers (β=-.060). Looking at the indirect effects, we found that affect, police legitimacy, and institutional trust fully mediated the associations between procedural/distributive justice and worry about crime and support for police powers. Table 5 shows the direct, indirect and total statistical effects of procedural/ distributive justice, community engagement and worry about crime on support for police powers, as well as the proportion mediated. As the table shows, the total effects of procedural/distributive justice and worry about crime were positive but fully mediated by affect and legitimacy/institutional trust (i.e. the direct effects of these variables on support for police powers were not significant). Effects decomposition showed that for procedural/distributive justice, the most important pathways ran through police legitimacy (β=.102) and affect (β=.086); for worry about crime, affect was the most influential mediator (β=.042). Table 5 also shows the indirect effects with affect as the outcome variable. Police legitimacy and institutional trust fully mediated the association between community engagement and affect, and partially mediated the association between procedural/distributive justice and affect. The most important pathway for community engagement ran through confidence in the MPS to handle the pandemic (β=.053); for procedural/distributive justice, all three mediators were significant with similar effect sizes.

4 We also regressed all variables on the demographic and experiential variables in Table 1, along with a variable representing the number of COVID-19 deaths in respondents’ boroughs at the time of interview. These paths are not shown in the figure for visual ease. 10 [Table 5 here]

Finally, we tested the SEM (using Montecarlo integration) before the first easing of lockdown and after. Given that the only real ‘break’ we found in study one pertained to police issuing fines, we focus only on public support for fines, so the final outcome variable was a single (ordinal categorical) indicator rather than a latent variable. The standardised regression coefficients for the key predictors of support for police issuing fines were consistent during the first two months of lockdown and after the first easing of restrictions: affect was β=.350 before and β=.304 after; obligation to obey the police was β=.166 before and β=.162 after; and confidence in the ability of the MPS to handle the pandemic was β=.088 before and β=.060 after.5 The standardised regression coefficients for the key predictors of affect were also consistent (procedural/distributive justice was β=.261 before and β=.216 after; obligation to obey the police was β=.062 before and β=.080 after; and confidence in the ability of the MPS to handle the pandemic was β=.132 before and β=.060 after), as was the case for key predictors of obligation to obey (procedural/distributive justice was β=.555 before and β=.642 after).

Discussion In the face of the COVID-19 pandemic, police services around the world have been granted unprecedented new powers to enforce social distancing restrictions to try to get the virus under control. Using data from a representative survey of Londoners conducted during the height of the first wave of the pandemic, the aims of this study were threefold: to measure the scale of public support for enhanced police powers; to consider changes in support over the study period; and to explore the factors that predict support for police empowerment. We found, first and foremost, that the overwhelming majority of Londoners supported giving the police some additional powers to deal with the COVID-19 crisis. But there seemed to be some boundaries to this support. While the majority of respondents were supportive of the police getting the power to issue fixed penalty fines and detain people, only a minority were supportive of police having the power to use facial recognition technology and track people’s mobile phones. Previous research in pre-COVID-19 times has shown that concerns around privacy are a strong predictor of the rejection of new police powers related to surveillance (Heen, Lieberman and Miethe, 2017; Sakiyama et al., 2017; Bradford et al., 2020); it could be that privacy concerns are also driving the lower levels of support for surveillance powers amongst our sample. Second, interrupted time series analysis showed that support for most of the police powers declined over time. Importantly, the first easing of the lockdown in England on 13 May appeared to act as a catalyst for the change in the trendlines observed for general support for police powers, detaining people and particularly fixed penalty notices, but not for live facial recognition or mobile phone tracking. These findings suggest that Londoners were willing to temporarily support police powers that were related to the pandemic effort, particularly the ability of the police to fine people who broke lockdown; however, this tentative support started to wane after the first easing of the lockdown. Compliance with lockdown restrictions in England during the first wave of the pandemic was generally high (Jackson et al., 2020), so the decline in support for police powers (especially the ability to fine people) following the first easing of the restrictions could simply be due to respondents realising that more intrusive powers were not necessary. Crucially, police legitimacy remained unchanged during the study period and the easing of the first lockdown led to concurrent declines in community engagement, trust in procedural/distributive justice and affective responses, suggesting the decline in support for police powers was not caused by declining trust and legitimacy. Another notable finding from study one is that some other events resulted in short-term changes in support (e.g. the Dominic Cummings scandal increased general support for police powers and George Floyd’s death decreased support for police enforcement powers); however, these effects were short-lived and did not impact the overall trend of attitudes. These findings indicate that events

5 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=0.097) 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=0.429). Please contact the first author for details. 11 which seem influential at the time might not have lasting impact on people’s perception of the police, at least for the perceptions studied here. Third, turning to the SEM results, we found support for a number of our hypotheses. Police legitimacy, confidence in the MPS to handle the pandemic, and particularly affect all had significant direct associations with support for police powers (H3 and H4). These findings are consistent with the extant research on police empowerment showing that people who grant police legitimacy are also more willing to empower them (Moule et al., 2019; Fox et al., 2020). Feeling a moral duty to obey police seems to mean that people are more willing to grant police the right to determine what powers are appropriate and acceptable. The finding for affect fits with research into public acceptance of new technologies which shows that when knowledge and experience are lacking, people’s emotional response to an issue drives their stance towards it (Siegrist et al., 2007; Midden and Huijts, 2009; Merk and Pönitzsch, 2017). It seems that, even in the midst of a pandemic, both during the first two months of ‘hard lockdown’ and in subsequent periods of the relaxation of restrictions, people relied on how they felt about giving police more power to inform their decision of whether or not to support police empowerment. Although there were no significant direct paths between trust and worry about crime and support for police powers (H1 and H2), there were multiple indirect effects. Affect, legitimacy and institutional trust mediated all of the modelled association between procedural/distributive justice and worry about crime and support for police powers (H5 and H6). The strongest indirect pathways ran from procedural/distributive justice through police legitimacy and affect. In other words, people who view the police as having the right intentions towards them are more likely to feel a moral obligation to obey police and respond more positively to police empowerment, which in turn influences their support for giving police new powers. These findings fit with research conducted in normal times. For example, Yesberg and Bradford (2019) found that affect fully mediated the relationship between trust in police fairness and support for a policy to arm more police officers. Bradford and colleagues (2020) found that police legitimacy fully mediated the relationship between trust in police fairness/engagement and acceptance of police use of live facial recognition. There was also an indirect path from worry about crime to support for police powers, through affect: those more worried about crime had more positive affective responses towards the new powers, leading to more support for police empowerment. However, the indirect effect of worry was less than half that of procedural/distributive justice. Prior research into police empowerment in normal times has found that normative judgements about police are more important predictors of support for police powers than instrumental concerns (Yesberg and Bradford, 2019; Bradford et al., 2020). In other words, trust in the people being empowered (i.e. police) seems to be more important than the ends toward which the power is oriented (i.e. a reduction in crime). Our findings lend support to this assertion and show that, even in the context of the pandemic, normative judgements play a superior role. Taken together, and consistent with the findings from Nix and colleagues’ (2020) study, our path analysis shows that similar factors motivate the acceptance of police empowerment in both pandemic and normal times.

Limitations There are, of course, a number of drawbacks to this study that must be acknowledged. First, we could only consider data from the height of the first wave of the pandemic. Data preceding and following these three months would have been useful to provide information about how quickly the increased support in police powers materialised and when these attitudes finally levelled off or picked up again during future waves. We were also limited by the variables available in the dataset. First, we only had one measure of police legitimacy: duty to obey. We were missing the other key component of the construct (normative alignment). Although duty to obey represents an internalised acceptance of the rightful authority of police to dictate appropriate behaviour, some research has shown that normative alignment is more important for predicting support for police power (Gerber and Jackson, 2017; Bradford et al., 2020). We were also missing a measure of trust in police effectiveness (e.g. whether people believe the police will turn up if called in an emergency), although previous research has shown that perceptions of procedural justice and fairness are more important predictors of police empowerment than perceptions of competency (Yesberg and Bradford, 2019; Bradford et al., 2020).

12 Future research should continue to explore the link between trust, legitimacy (and their component parts) and police empowerment. Other limitations include the use of cross-sectional data, which limits our ability to make causal claims based on these results. Longitudinal data, where changes in attitudes of the same individuals are tracked over time, would have been instructive in this regard. Lastly, although our findings provide a unique snapshot into support for police powers during what is an unprecedented public health crisis, we do not know whether the same predictors will generalise to police empowerment in normal times. Our findings do, however, fit with most previous research on the topic of police empowerment, indicating that people rely on similar thought processes when considering their attitudes about the police during crises and normal times.

Conclusion In this study we have shown that, even in unprecedented times, people’s support for police powers is rooted in trust, legitimacy and affect. Levels of trust, legitimacy and affect were stable over time, suggesting that the enforcement as last resort policy seemed to have worked. There was one notable ‘event effect’, where the first easing of lockdown seemed to bring with it a sense that the police no longer need to use fines to enforce lockdown requirements. But aside from that, our analysis suggesting that people rely both on their emotional response to new police powers and their perceptions about the people being empowered fits with previous research into police empowerment in normal times. Taken together, our findings highlight that people’s perceptions of police, particularly whether those officers act in respectful ways and make neutral and accountable decisions as they engage in everyday policing, can have important implications for the acceptance of new police powers. It seems that, even in the midst of a pandemic, people rely on how they feel about giving police more power to inform their decision of whether or not to support police empowerment.

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15 Table 1. Sample characteristics N (unweighted) Gender Female 1794 Male 1405 Age 15-24 306 25-44 745 45-64 1546 65+ 603 Ethnic group Asian 444 Black 390 Mixed 116 White 2035 Other 65 Victim of crime related to covid Yes 189 No 2999 Stopped by police during covid Yes 144 No 3050 Total 3201

16 Table 2. Interrupted time series analysis of changes in support for police powers (Newey-West standard errors and one lag)

General Fixed Detaining Live Facial Mobile support penalty people Recognition tracking Dominic Cummings scandal (13 April) -0.023* -0.006 -0.015 0.033 0.024 Pre-intervention trend [0.009] [0.027] [0.025] [0.023] [0.017] 0.329** 0.117 0.229 -0.279 -0.258 Day of intervention [0.104] [0.156] [0.188] [0.150] [0.182] Change in trend vs pre- 0.013 -0.001 0.006 -0.037 -0.027 intervention [0.009] [0.027] [0.069] [0.023] [0.017] -0.010*** -0.007*** -0.009*** -0.004* -0.002 Post-intervention trend [0.002] [-0.001] [0.002] [0.002] [0.002] Propensity score*Sampling -0.029 0.029 0.009 0.085 -0.016 weight [0.041] [0.045] [0.069] [0.093] [0.063] 4.606*** 4.269*** 3.778*** 2.926*** 2.645*** Constant [0.080] [0.197] [0.167] [0.197] [0.115] First easing of lockdown (13 May) -0.001 -0.001 -0.003 -0.009 -0.006 Pre-intervention trend [0.002] [0.003] [0.003] [0.004] [0.005] -0.137 -0.200* 0.040 0.019 0.132 Day of intervention [0.099] [0.093] [0.135] [0.164] [0.184] Change in trend vs pre- -0.011* -0.004* -0.009* 0.070 0.002 intervention [0.005] [0.002] [0.004] [0.006] [0.007] -0.011* -0.005* -0.012*** -0.002 -0.004 Post-intervention trend [0.005] [0.002] [0.003] [0.005] [0.004] Propensity score*Sampling -0.014 0.039 0.020 0.085 -0.024 weight [0.042] [0.044] [0.073] [0.095] [0.061] 4.49*** 4.232*** 3.742*** 3.186*** 2.797*** Constant [0.064] [0.092] [0.091] [0.110] [0.095] Death of George Floyd (26 May) -0.005** -0.003 -0.001 -0.007 -0.003 Pre-intervention trend [0.002] [0.002] [0.003] [0.004] [0.003] -0.054 -0.273** -0.246* 0.080 0.006 Day of intervention [0.106] [0.092] [0.122] [0.170] [0.145] Change in trend vs pre- -0.009 0.005 -0.006 0.005 -0.001 intervention [0.008] [0.004] [0.006] [0.007] [0.006] -0.0136 0.002 -0.007 -0.002 -0.003 Post-intervention trend [0.008] [0.003] [0.005] [0.005] [0.006] Propensity score*Sampling -0.013 0.039 0.018 0.085 -0.025 weight [0.043] [0.047] [0.074] [0.095] [0.061] 4.557*** 4.278*** 3.711*** 3.166*** 2.746*** Constant [0.070] [0.086] [0.092] [0.113] [0.099] Second easing of lockdown (1 June) -0.005*** -0.006** -0.004 -0.006* -0.002 Pre-intervention trend [0.001] [0.002] [0.003] [0.003] [0.002] -0.066 -0.141 -0.211 0.042 -0.030 Day of intervention [0.123] [0.102] [0.135] [0.159] [0.139] Change in trend vs pre- -0.009 0.007 -0.002 0.006 0.001 intervention [0.012] [0.005] [0.007] [0.008] [0.008]] -0.014 0.002 -0.002 -0.001 -0.002 Post-intervention trend [0.012] [0.004] [0.006] [0.007] [0.008] Propensity score*Sampling -0.013 0.038 0.017 0.085 -0.025

17 weight [0.043] [0.045] [0.073] [0.096] [0.061] 4.573*** 4.324*** 3.756*** 3.150*** 2.744*** Constant [0.066] [0.084] [0.090] [0.104] [0.102] Third easing of lockdown (15 June) -0.008*** -0.007*** -0.006*** -0.005** -0.003 Pre-intervention trend [0.001] [0.001] [0.002] [0.002] [0.001] 0.185 0.033 0.050 -0.082 -0.065 Day of the intervention [0.199] [0.101] [0.122] [0.154] [0.175] Change in trend vs pre- -0.038 0.010 -0.018 0.022 0.010 intervention [0.038] [0.009] [0.014] [0.019] [0.022] -0.046 0.003 -0.025 0.017 0.008 Post-intervention trend [0.039] [0.009] [0.014] [0.019] [0.022] Propensity score*Sampling -0.013 0.042 0.023 0.084 -0.023 weight [0.042] [0.045] [0.071] [0.095] [0.060] 4.623*** 4.350*** 3.809*** 3.120*** 2.750*** Constant [0.068] [0.079] [0.090] [0.098] [0.104]

18 Table 3. Interrupted time series analysis of changes in support for trust, police legitimacy and affect (Newey-West standard errors and one lag)

Procedural & Community Police Affective distributive engagement legitimacy response justice Dominic Cummings scandal (13 April) 0.0190 0.009 0.011 0.002 Pre-intervention trend [0.279] [0.020] [0.010] [0.007] 0.043 0.156 -0.001 0.063 Day of intervention [0.172] [0.145] [0.112] [0.068] Change in trend vs pre- -0.024 -0.016 -0.016 -0.008 intervention [0.028] [0.020] [0.010] [0.007] -0.005*** -0.008*** -0.005 -0.006*** Post-intervention trend [0.001] [0.001] [0-004] [0.001] Propensity score*Sampling 0.019 0.023 -0.034 0.016 weight [0.022] [0.022] [0.024] [0.021] 3.663*** 4.102*** 4.636*** 2.509*** Constant [0.186] [0.134] [0.075] [0.055] First easing of lockdown (13 May) 0.001 0.005 -0.001 -0.001 Pre-intervention trend [0.003] [0.003] [0.001] [0.002] 0.054 -0.077 0.175 0.001 Day of intervention [0.099] [0.089] [0.148] [0.071] Change in trend vs pre- -0.011** -0.017*** -0.011 -0.008* intervention [0.004] [0.004] [0.009] [0.003] -0.010** -0.012*** -0.012 -0.008* Post-intervention trend [0.003]] [0.003] [0.009] [0.003] Propensity score*Sampling 0.014 0.022 -0.040 0.014 weight [0.021] [0.020] [0.028] [0.019] 3.789*** 4.117*** 4.687*** 2.522*** Constant [0.070] [0.068] [0.047] [0.039] Death of George Floyd (26 May) 0.001 0.001 0.001 -0.001 Pre-intervention trend [0.002] [0.002] [0.001] [0.001] -0.064 -0.132 0.107 -0.046 Day of intervention [0.101] [0.092] [0.185] [0.064] Change in trend vs pre- -0.011 -0.015** -0.019 -0.008 intervention [0.006] [0.005] [0.017] [0.004] -0.010 -0.014** -0.019 -0.009* Post-intervention trend [0.006] [0.005] [0.017] [0.004] Propensity score*Sampling 0.015 0.018 -0.035 0.014 weight [0.020] [0.020] [0.025] [0.019] 3.789*** 4.171*** 4.664*** 2.537*** Constant [0.057] [0.055] [0.040] [0.032] Second easing of lockdown (1 June) 0.001 -0.001 -0.001 -0.002** Pre-intervention trend [0.002] [0.002] [0.001] [0.001] -0.067 -0.170 0.221 -0.036 Day of intervention [0.110]] [0.104] [0.213] [0.068] Change in trend vs pre- -0.011 -0.013 -0.028 -0.009 intervention [0.008] [0.007] [0.024] [0.006] -0.011 -0.013 -0.029 -0.011 Post-intervention trend [0.007] [0.007] [0.024] [0.006]

19 Propensity score*Sampling 0.015 0.018 -0.033 0.014 weight [0.021] [0.020][ [0.024] [0.020] 3.810*** 4.201*** 4.683*** 2.552*** Constant [0.054] [0.054] [0.037] [0.032] Third easing of the lockdown (15 June) -0.003* -0.004** -0.001 -0.004*** Pre-intervention trend [0.001] [0.001] [0.001] [0.001] 0.092 -0.033 0.420 0.071 Day of intervention [0.118] [0.111] [0.353] [0.097] Change in trend vs pre- -0.039 -0.025 -0.105 -0.029 intervention [0.022] [0.018] [0.081] [0.018] -0.041 -0.029 -0.106 -0.032 Post-intervention trend [0.022] [0.018] [0.081] [0.018] Propensity score*Sampling 0.013 0.017 -0.041 0.002 weight [0.023] [0.020] [0.029] [0.021] 3.856*** 4.281*** 4.701*** 2.596*** Constant [0.051] [0.054] [0.039] [0.030]

20 Table 4. Pairwise correlations 1 2 3 4 5 6 7 8 Worry about crime (1) 1 Community engagement (2) -0.41 1 Procedural/distributive justice (3) -0.16 0.87 1 Confidence in the MPS (4) -0.16 0.22 0.53 1 Trust in government (5) -0.15 0.46 0.48 0.36 1 Police legitimacy (6) 0.04 0.35 0.50 0.27 0.30 1 Affective response (7) 0.04 0.31 0.41 0.35 0.31 0.28 1 Support for police powers (8) 0.08 0.38 0.55 0.36 0.32 0.45 0.56 1

21 Table 5. Direct, indirect and total effects calculated from path model in Figure 3 Support for police Affect powers β SE(B) β SE(B) Procedural/distributive justice

Direct 0.180** [0.055] 0.116 [0.063]

Total indirect 0.128*** [0.021] 0.275*** [0.037]

% mediated 41.5% 70.3%

Total 0.308*** [0.052] 0.391*** [0.077]

Community engagement

Direct 0.022 [0.045] -0.014 [0.053]

Total indirect 0.078*** [0.019] 0.064 [0.036]

% mediated 78% N/A

Total 0.100* [0.048] 0.051 [0.074]

Worry about crime

Direct 0.088** [0.025] 0.031 [0.028]

Total indirect -0.002 [0.008] 0.051*** [0.015]

% mediated N/A 62.1%

Total 0.085** [0.025] 0.082* [0.033]

Easing of first lockdown

Direct -0.029 [0.025] -0.060** [0.022]

Total indirect -0.043*** [0.011] -0.051** [0.018]

% mediated 11.6% 45.9%

Total -0.073** [0.027] -0.111*** [0.027]

***p<.001 **p<.01 *p<.05

22 Figure 1. Conceptual map

23 Figure 2. Interrupted time series analysis charts of support for police powers after the first easing of the lockdown in England (13 May 2020)

24 25 Figure 3. Interrupted time series analysis charts of community engagement, procedural/distributive justice, police legitimacy, and affective response after the first easing of the lockdown in England (13 May 2020)

26 Figure 4. Results from a structural equation model predicting support for police powers

27 Appendix A. Robustness checks for interrupted times series analysis As the first robustness check, we used a method proposed by Cruz et al. (2017, 2018), which they call ‘robust interrupted time series’ analysis. This approach takes a data-driven strategy in identifying the change point for the analysis which might come before or after the date of the intervention. According to this method, the estimated change point for general support for police powers, support for fixed penalty fines, procedural/distributive justice, and affective response fell on 11 May, on 14 May for support for detaining people, and on 17 May for community engagement. For direct comparison with the above analysis, we reran the models. As shown in Table A1 and Figure A1, the results and trends remained largely unchanged. We carried out a further robustness check, where we considered multiple interventions simultaneously, such as the impact on the trendline of multiple easings of the lockdown (Linden and Arbor, 2015). We have fitted models in close to thirty different configurations, still, only 13 May appeared to produce any statistically significant changes when multiple cut-offs were considered. Due to the high number of models, they are not included in this paper, but the code and results for the analyses are available from the authors upon request. Finally, it is worth mentioning that considering multiple single cut-off points for an interrupted time series analysis (like we did in Table 2) is a robustness check commonly suggested for interrupted time series (Linden and Adams, 2011). In that tradition, our results would be indicative that 13 May was likely an appropriate choice. There are three conclusions which can be drawn from these robustness tests. First, it seems likely that the prime minister’s speech on 10 May could have started changing minds even before the regulations changed on 13 May. By contrast, the change point for support for detaining people and community engagement only occurred a bit later, after people had a chance to potentially experience their increased freedoms. Second, these days were still close to the cut-off point we defined a priori. The proximity of these days and the largely unchanged results imply that adopting 13 May as the change point stands up to scrutiny. Finally, the analyses with multiple change points suggest that there was only a single event that changed attitudes towards police powers, which was the first easing of the lockdown. The trendline appeared to be unaffected before or after that event.

Table A1. Interrupted Time Series Analysis table of support for police powers with the date specified as suggested by Cruz et al. (2017)

Procedural General Fixed Detaining Community & Affective support penalty people engagement distributive response (11 May) (11 May) (14 May) (17 May) justice (11 May) (11 May) Models at the change point suggested by Cruz et al. (2017, 2018) 0.001 0.001 -0.002 0.001 0.006 -0.001 Pre-intervention trend [0.003] [0.003] [0.003] [0.002] [0.003] [0.002] -0.118 -0.201* -0.041 0.094 -0.067 0.012 Day of the intervention [0.100] [0.090] [0.136] [0.082] [0.076] [0.074] Change in trend vs pre- -0.012* -0.006* -0.009* -0.013** -0.019*** -0.008* intervention [0.005] [0.003] [0.004] [0.004] [0.004 [0.003] -0.011** -0.005** -0.011** -0.012** -0.012*** -0.008** Post-intervention trend [0.004] [0.002] [0.003] [0.004] [0.003] [0.002] Propensity score * Sampling -0.038 -0.054 -0.040 0.056 0.064** 0.014 weight [0.049] [0.048] [0.045] [0.023] [0.023] [0.019] 4.510*** 4.336*** 3.787*** 3.740*** 4.043*** 2.521*** Constant [0.074] [0.106] [0.110] [0.073] [0.069] [0.042]

28

Figure A1. Interrupted Time Series Analysis charts of support for police powers, community engagement, and procedural/distributive justice, and affective response with the date specified as suggested by Cruz et al. (2017)

29 Appendix B. Latent constructs and measures Factor Results from a four factor solution with no cross-loadings loading Support for police powers (‘To what extent do you support or oppose the Metropolitan Police Service…’ high = support) Getting extra powers to deal with Coronavirus 0.809 Issuing fixed penalty fines to enforce Government Coronavirus measures 0.775 Detaining people to enforce Government Coronavirus measures 0.668 Trust in community engagement (‘To what extent do you agree with these statements about the police in your area?’ high = agree) They can be relied on to be there when you need them 0.654 They are dealing with the things that matter to people in this community 0.853 They listen to the concerns of local people 0.771 Trust in procedural/distributive justice (‘To what extent do you agree with these statements about the police in your area?’ high = agree) They would treat you with respect if you had contact with them for any reason 0.688 They treat everyone fairly regardless of who they are 0.819

Worry about crime (‘To what extent are you worried about…’ high = more) Crime in your area? 0.772 Anti-social behaviour in your area? 0.777 Fit statistics Chi square 8295.91 Degrees of freedom 55 p-value <0.001 Root mean square error of approximation 0.024 CFI 0.992 TLI 0.987

30