1. Introduction
Minorities have limited leverage at the ballot box: their turnout and vote choices can be outweighed by those of larger groups. For their preferred candidates to be elected, minorities need other groups to mobilize and vote for them. We argue that riots can help minorities achieve this: riots publicly communicate the challenges these groups face, and this information can induce larger groups to turn out and support the candidates more likely to address these problems.Footnote 1 In this way, riots can be politically salient despite their often inchoate nature.
Enos et al. (Reference Enos, Kaufman and Sands2019) examine citizen responses after the Los Angeles riot of 1992 and find that the attitudes and behaviors of both white and Black voters moved in a progressive direction, with increased registrations and greater voter support in ballot initiatives.Footnote 2 Yet it is possible for individuals to change their views on specific issues without changing their choice of candidate. Gillion (Reference Gillion2020) looks at voting in elections and finds that local protests in Democratic areas increased turnout and support for the Democrats. He interprets this as evidence that the “silent majority” was sympathetic to the rioters. This however does not show whether riots change voting behavior in a way that can lead to the election of more sympathetic politicians. For that, we need to look at a case where the incumbent was seen as hostile to the rioters. This article focuses on one such case.
We test whether the 2011 London riots changed voting behavior in the 2012 election for the mayor of London. There are important reasons to expect that previous findings, for example from Los Angeles in 1992 (e.g., Enos et al., Reference Enos, Kaufman and Sands2019), will not apply to London in 2011: the riots happened several decades apart and London has a different racial history. It is also a less segregated city than Los Angeles. Therefore our study also tests whether previous findings are generalizable.
Following the 2011 London riots, voters concerned with law and order would likely have favored the incumbent Conservative mayor, Boris Johnson, whose party is closely associated with law and order. Therefore backlash resulting from a negative reaction to the violence (Feinberg et al., Reference Feinberg, Willer and Kovacheff2017) should have resulted in an increase in support for the Conservative incumbent.Footnote 3 On the other hand, the Conservative party is associated with post-2008 financial crisis austerity and is generally seen as less sympathetic toward ethnic minorities and the poor. Consequently, a change in attitude in favor of the rioters should have manifested itself in a shift of the vote away from the Conservative Party. From this logic, the Conservative Party's vote share can be used to assess whether the riots strengthened or weakened the electoral prospects of candidates favorable to the rioters.
In this article we measure exposure to the riots as physical proximity to the riot locations or to where the rioters lived, since the rioters’ most likely allies are those individuals who were physically proximate to the events. These two measures likely capture different mechanisms: proximity to riot location would have provided first-hand information, while proximity to the homes of rioters would have provided information through social interactions and local networks. Exposure to a riot captures an average between positive and negative responses; e.g., some people are concerned and/or fearful and vote Conservative, others become informed and switch their vote away from the Conservatives. The size of these groups and of their responses will depend on the type of proximity, i.e., whether it is to riot location or to rioter residence.
We deploy a difference-in-differences specification that takes advantage of the localized data that are generated by riots that happen in urban areas (Dancygier, Reference Dancygier2010). We estimate treatment effects on each electoral ward, and find that exposure to the riots increased turnout and reduced the Conservative party's vote share in the first election after 2011.Footnote 4 Since the Conservative party was associated with austerity and seen as the party of law and order, these shifts are consistent with our hypothesis that the riots helped mobilize voters in favor of parties more sympathetic to the rioters.
To delve deeper into our results, we turn to ecological inference estimates and focus on the behavior of white voters. We find that our aggregate results are in part due to white voters who turned out more and reduced their votes for the incumbent Conservative mayor as a result of exposure to the riots (where exposure is defined as either living close to where the riots happened or to where the rioters lived).Footnote 5
2. Case selection and research question
The 2011 London riots followed the shooting by police of a Black suspect, Mark Duggan, on 4 August, causing a protest near the site of the shooting that turned into generalized unrest across London and other parts of England. The riots lasted several days, generating substantial media and political attention and leading to damage to property, numerous arrests, and subsequent criminal prosecutions. Spatial research shows that rioters conformed to the rational model of decision-making, with riots occurring close to areas of deprivation (Baudains et al., Reference Baudains, Braithwaite and Johnson2013b; Kawalerowicz and Biggs, Reference Kawalerowicz and Biggs2015; Leon-Ablan and Kawalerowicz, Reference Leon-Ablan and Kawalerowicz2021).Footnote 6
We take media reports of riots and geocode their locations. Our unit of observation is the electoral ward, and we use data from three mayoral elections: turnout for 2008 and 2012, and vote shares by party for 2004, 2008, and 2012. The mayoral election is London-wide, using the supplementary vote (SV) system, which means that both first and second preferences count. We have data of 3552 riot-related arrests from the Metropolitan Police. Details of sources and variable constructions are in the online Appendix.
3. Identification and specification
We use a difference-in-differences estimator, where the “policy change” is the riots in 2011, with treatment status defined as either proximity to the riots or proximity to where the rioters lived.Footnote 7 Riots are likely to change the behavior of those who are more closely exposed (Gillion, Reference Gillion2020). For example, those who observe the violence first-hand or know it is happening near their homes will respond differently from those whose exposure is exclusively through television news reports. In short, we exploit variation in treatment intensity induced by distance to riot location and rioter residence.
We construct a treatment group of electoral wards with centroids between 0.5 and 3 km of the locations where the riots happened, and a control group of all electoral wards with centroids within 3 and 5.5 km of where riots happened.Footnote 8 We exclude all areas within 500 m of the riots to address the concern that riots may have happened in particular types of areas (e.g., those with a large ethnic minority population or with a large number of low-skilled workers).Footnote 9 Figure 1 illustrates this identification strategy. Our second treatment is proximity to rioter residence, defined as all wards that had at least one resident charged for rioting. The control group is all wards between 0.5 and 5.5 km of the riots that had no residents charged for riot-related offences.Footnote 10 Figure 2 illustrates this identification strategy.
Fig. 1. Treatment 1 (proximity to the riots). The inner ring shows a 500 m buffer around the location of the riots. The treatment areas are between the inner and middle rings, and the control areas are between the middle and outer rings.
Fig. 2. Treatment 2 (proximity to where the rioters lived). The stars pinpoint the riot locations. Treatment wards are those that had at least one arrest, and are shaded dark in the map. The control wards are those with no arrests, and are not shaded. Only data within the bold rings are used in the estimation.
The identifying assumption is that the data exhibit common trends: in the absence of the riots, vote shares and turnout would have evolved in the same way in the treatment and control wards (conditional on the controls). The riots took place in a number of locations across the city, and London is a patchwork of many small neighborhoods that vary substantially in terms of income and ethnic and social composition (Manley and Johnston, Reference Manley and Johnston2014); therefore both our treatment and control groups include poor and rich areas, more and less diverse areas, and areas with high and low concentrations of working class residents. Figures A1 and A2 in the online Appendix plot the data for the pre-riots period and show that trends were parallel. A violation of the common trends assumption when treatment is proximity to the riot locations would require that areas close to the riots shift away from the Conservative Party faster than other areas. Looking at Figure 1, the treated areas are those between the inner and middle rings. They follow no clear spatial pattern other than being equidistant from riot locations, and it is difficult to think of what changes these areas could have experienced relative to the control areas (wards between the middle and outer rings). A violation of the common trends assumption when treatment is proximity to the homes of rioters would require that the areas in dark in Figure 2 follow different trends or experience different shocks between 2008 and 2012. However, the dark and light-colored areas in the map follow no clear spatial pattern, making shocks of the required type unlikely.
Table A3 checks whether treatment areas saw a greater (or smaller) shift toward Labour in the 2008 election.Footnote 11 There is no evidence that this was the case, reassuring us that vote choices in treatment and control areas were evolving in similar ways prior to the 2011 riots.
To look at whether treated areas changed their voting behavior more than control areas, we estimate the following equation:
We consider two outcomes: turnout in ward i in election t and the share of the votes cast for the Conservative mayor in ward i in election t. Depending on the specification, the variable treatmenti is either (i) a dummy that equals 1 if ward i was between 0.5 and 3 km of a riot and 0 if it was between 3 and 5.5 km (all other wards are dropped), or (ii) a dummy that equals 1 if at least one resident of i was arrested and 0 otherwise (looking only at wards between 0.5 and 5.5 km from a riot). The variable postt equals 1 for elections after the 2011 riots, and 0 otherwise.Footnote 12 The coefficient on the interaction between these two variables is the difference-in-differences estimate. We also include controls measured in 2011, and borough and year effects. We adjust the standard errors to take account of spatial and serial correlation following the procedure in Conley (Reference Conley1999).Footnote 13
4. Results
Table 1 shows the main results. We find that the riots generated a positive effect on turnout (columns 1 and 2) in the treated areas (those close to a riot or to a rioter residence). They also led to a larger decrease in the fraction of the vote received by the Conservative candidate in treated areas: the interaction shows that in treatment wards the Conservative vote share fell 1.5 percentage points more (column 3) or 2.2 percentage points more (column 4) than it did in control wards. Given that the election was won by the Conservative candidate Boris Johnson with 51 percent of the vote against Labour's 48.5 percent, the effects are quantitatively large.Footnote 14
Table 1. Turnout and Conservative vote share as a function of exposure to riots
Notes: Standard errors in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05, +p <0.10. Standard errors are adjusted for spatial correlation following the procedure in Conley (Reference Conley1999). Variable definitions and sources can be found in the online Appendix.
These results are robust to considering as treated only those wards with more than the median number of charged individuals (Table A6 in the online Appendix) and to changing the size of the treatment and control groups (Tables A7, A8, and A9). The results are also robust to two placebo tests. The first takes as treated those areas near Charing Cross station (the center of London) and as control those areas that are farther away (Table A10 and Figure A7), and finds no effect. The second placebo test uses propensity score matching to find areas similar to those that experienced riots, but that did not experience any unrest. Treatment is then defined on the basis of distance to these alternative riot locations (Table A11 and Figure A8). Again, this placebo test does not replicate the results in Table 1.
4.0.1 Ecological inference results
To explore our results in more detail, we examine whether the changes we found were driven by the behavior of a particular subgroup of the population. We do this by replicating the estimation for white and for Black voters, using ecological inference.Footnote 15 Table 2 shows the results: we find evidence that treatment has a positive effect on the turnout of white voters, who also switch away from the Conservative party at a faster rate than whites in control areas. Black voters also switch away from the Conservatives, but there is no evidence of a change in Black turnout.Footnote 16 Following Bobo and Gilliam (Reference Bobo and Gilliam1990), the relative lack of empowerment felt by Black voters may explain the low and unchanging turnout. It is also possible that, consistent with signaling theory (e.g., Gause, Reference Gause2022), the riots conveyed no new information to these voters.
Table 2. Turnout and Conservative vote share as a function of exposure to riots, by ethnicity
Notes: Standard errors in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.10. Standard errors are adjusted for spatial correlation following the procedure in Conley (Reference Conley1999). Variable definitions and sources can be found in the online Appendix.
5. Conclusion
We contribute to understandings of the effect of political violence on voting behavior. Drawing on the work of Enos et al. (Reference Enos, Kaufman and Sands2019), we hypothesized that riots can raise voters’ awareness of the problems and challenges faced by the groups that riot, and that this translates into electoral support for candidates with more progressive objectives. Our case involves a vote where the incumbent was seen as unsympathetic to the rioters, and therefore extends Enos et al. (Reference Enos, Kaufman and Sands2019)'s work on ballot initiatives to the core democratic process of electing a representative. Our findings from the 2011 London riots confirm our expectations. We show that it is not just proximity to the riots that matters, but also closeness to where rioters lived. Future work should delve deeper into the mechanisms that connect these two measures of exposure to the changes in voting behavior they generate. Finally, we uncover some evidence suggesting the results are partly due to a change in the behavior of white voters, both in terms of turnout and vote choice. Black voters shift their vote away from the Conservatives, but show no change in turnout; we hypothesize that this is due to a feeling of effective (if not formal) disenfranchisement. Further work should address this issue.
In a period of increasing minority protest and violence, these dynamics may point out the future nature of electoral politics. Rather than the African American voting blocks identified by Fording (Reference Fording1997; Reference Fording2001) as a result of the 1960s riots, recent riots have a more heterogeneous and cross-racial response. In the era of Black Lives Matter, there may be long-term consequences for voting behavior, mobilizing white voters in support progressive parties.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/psrm.2022.49. Replication https://doi.org/10.7910/DVN/UNFADD
Acknowledgments
We acknowledge King's College for support. We thank Damien Bol, Jens Olav Dahlgaard, and Adam Fagan for their excellent comments, and Jonathan Higgs and Jessica Pate for research assistance.
Open access
