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Do Racial Justice Frames Increase Support Among Democratic Constituencies? Evidence from Two Survey Experiments during the 2020 Georgia Senate Runoffs

Published online by Cambridge University Press:  11 December 2025

Tabitha Bonilla Open the ORCID record for Tabitha Bonilla [Opens in a new window]  and
Alvin B. Tillery Jr Open the ORCID record for Alvin B. Tillery Jr [Opens in a new window]
Tabitha Bonilla*
Affiliation:
Human Development and Social Policy, Political Science, and Institute for Policy Research, Northwestern University, Evanston, IL, USA
Alvin B. Tillery Jr
Affiliation:
Political Science and African American Studies (by courtesy), Northwestern University, Evanston, IL, USA
*
Corresponding author: Tabitha Bonilla; Email: tabitha.bonilla@northwestern.edu

Abstract

Do appeals to Black voters necessarily detract white voters from supporting the left? Extant studies have yielded mixed answers to this question by examining voter turnout data. We use two survey experiments to test how framing politicians as either supportive of or hostile to the #BlackLivesMatter (BLM) and #SayHerName (SHN) movements affected the willingness of voters to support them during the 2020 Senate runoff elections in Georgia. We find that Democratic-leaning respondents in both a national sample of Black respondents and a sample of White respondents in Georgia were more likely to support politicians whom we framed as supportive of the BLM and SHN movements. These findings illustrate the potential potency of messaging strategies grounded in racial justice themes for mobilizing Democratic-leaning voters in American elections.

Keywords

Black Lives Matter (BLM)Say Her Name2020 electionsGeorgiaSurvey experimentIdentity politicsRacial appeals

Information

Type
Research Note
Information
Journal of Race, Ethnicity, and Politics , First View , pp. 1 - 11
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of the Race, Ethnicity, and Politics Section of the American Political Science Association

Introduction

Since #BlackLivesMatter (BLM) was posted in July 2013, the hashtag has been reposted more than 30 million times and served as the banner of two waves of sustained protests calling for an end to state violence against Black Americans and greater racial equity (Jackson and Foucault Welles Reference Jackson and Foucault Welles2016; Garza Reference Garza2014; Rickford Reference Rickford2016; Taylor Reference Taylor2016, 13–15). Whereas the first protest wave (starting with the Ferguson uprising) was centered in Black communities and comprised mostly of Black participants (Ransby Reference Ransby2015; Rickford Reference Rickford2016; Taylor Reference Taylor2016; Boyles Reference Boyles2019; Cobbina Reference Cobbina2019), the second wave drew participants from every segment of American life and was likely the largest mass mobilization in American history (Kaiser Family Foundation 2020; Thomas and Horowitz Reference Thomas and Horowitz2020). While the second wave began in Spring 2020, it began to dissipate by December 2021 (Burch Reference Burch2020). Nonetheless, it aimed to play a more direct role in electoral politics (Ramirez Reference Ramirez2020; Gillion Reference Gillion2020; King Reference King2020). During the Georgia Senate runoff elections, BLM activists formed a 527 group or “Super PAC” (political action committee) to promote racial justice messages in support of the Democratic candidates in the election (King Reference King2020). We ask: Do racial justice messages—like the ones deployed by the Black Lives Matter PAC —demobilize Democratic-leaning white AmericansFootnote 1 in elections? Do these political statements increase support among Black voters?

While theories of descriptive representation and group appeal suggest that these types of appeals will excite Black voters and increase support (Gay Reference Gay2002; Baker and Cook Reference Baker and Cook2005), some highlight concerns for backlash among white voters, even those who tend toward more progressive values (Marans Reference Marans2020; Tavernese and Eligon Reference Tavernese and Eligon2020; Thrush Reference Thrush2020). There is also some evidence that highlighting particular Black communities may detract from popularity because there is variation in support for more intersectional movement claims (Bunyasi and Smith Reference Bunyasi and Smith2019; Bonilla and Tillery Reference Bonilla and Tillery2020). To test these theories, we conducted two survey experiments during the height of the 2020 Georgia Senate runoff election between November 8, 2020, and January 5, 2021. One experiment seeks to understand the effect of these statements on white, Independent, and Democratic voters in Georgia, who are more likely to see these appeals. The second experiment seeks to understand the effect of these statements through an experiment on a Black national sample. Both experiments used frames that described politicians involved in the election as either supportive of or hostile to the BLM movement, and an offshoot movement, the #SayHerName (SHN) campaign, focused on raising the visibility of Black women victims of police violence who are ignored at higher rates than Black men (Borda and Marshall Reference Borda and Marshall2020; Lindsey Reference Lindsey2018; Brown et al., Reference Brown, Rashawn, Summers and Fraistat2017; Crenshaw, Reference Crenshaw, Ritchie, Anspach, Gilmer and Harris2015; McMurtry-Chubb, Reference McMurtry-Chubb2015).

We find that the white sample was not negatively affected by the BLM and SHN messages as the backlash hypothesis would predict. And, Black voters were mobilized by the BLM and SHN messages, consistent with literature on racial appeals. These findings suggest that messaging strategies grounded in racial justice themes hold the potential to serve as potential mobilizers for Black voters without alienating white voters in American elections.

The White Backlash Versus Democratic Mobilization Debate

Among the left, much debate has examined what to do about “identity politics,” with a frequent suggestion that appeals to Black voters (and other historically excluded groups) turn away other voters from the party. Certainly, the racial appeals literature contrasts how Black and white voters distinctly respond to issues of racial justice (Stephens-Dougan Reference Stephens-Dougan2021). For their part, Black Americans, who have long been thought of as a singular voting bloc, tend to increase support for candidates and turnout when issues specifically appeal to social justice issues (Gleason and Stout Reference Gleason and Stout2014). Given the divergent racialized history, white Americans respond with more nuance; those with higher levels of racial resentment tend to reject Black racial appeals, while those with lower amounts of racial resentment are ambivalent or supportive of Black racial appeals (Hajnal Reference Hajnal2006; Mendelberg Reference Mendelberg2009; Karpowitz et al. Reference Karpowitz, King-Meadows, Monson and Pope2021). Because white voters with lower levels of racial resentment tend to be Democrats (Abramowitz and McCoy Reference Abramowitz and McCoy2019), our question hones in on a narrow portion of white voters in contrast to Black voters: do appeals to Black voters necessarily discourage Independent or Democratic white voters? We leverage the 2020 Georgia runoff election—which was central to establishing a Democratic Senate majority—to examine voter motivation to support or reject candidates deemed to be allies or enemies of those movements. Several media outlets circulated stories between the November election and the runoff election that advanced the narrative that “moderate” and white Democrats would react negatively toward the Democrats due to Republican attack ads featuring Democratic support of BLM (Marans Reference Marans2020; Tavernese and Eligon Reference Tavernese and Eligon2020; Thrush Reference Thrush2020). While data have not yet emerged to support these claims, it is important to note that there is a long history of white backlash at the polls in response to debates and protests over issues relating to racial equity in America (Abrajano and Hajnal Reference Abrajano and Hajnal2015; Gillion Reference Gillion2020, 137–138; Wasow Reference Wasow2020). Public opinion studies have also found that there is a sizable Black-White divide in support for the BLM movement (Horowitz and Livingston Reference Horowitz and Livingston2016; Thomas and Horowitz Reference Thomas and Horowitz2020) and beliefs about the role that race plays in campaigns more generally (e.g., Stephens Dougan Reference Stephens-Dougan2020, Reference Stephens-Dougan2023; Bonilla et al Reference Bonilla, Filindra and Lajevardi2022). Considering this context, it is certainly a plausible hypothesis that the BLM and SHN movements prompted a backlash among white voters during the 2020 election cycle (e.g., Beyerlein and Andrews Reference Beyerlein and Andrews2008; Gillion and Soule Reference Gillion and Soule2018).

Political scientists typically view past behavior as an important benchmark for understanding voting in future elections (Converse and Markus Reference Converse and Markus1979; Traugott and Tucker Reference Traugott and Tucker1984). Given this axiom, it is important to note that existing empirical studies of how support for the BLM movement shaped voting behavior in a US election did not substantiate the white backlash thesis (Gillion Reference Gillion2020; Klein Teeselink and Melios Reference Klein Teeselink and Melios2025). Although Gillion (Reference Gillion2020) found that support for the BLM movement among white Americans tracked with their political ideology—with those identifying as “liberal” being more likely to support the movement than “moderates” or “conservatives”—these feelings did not translate into motivations for white voters to support one candidate over another in the 2016 presidential election (151). Gillion also found that “African Americans who expressed strong positive sentiment for BLM were more likely to vote in the 2016 elections” (152). Given the close association between Republican partisanship and elevated commitments to white racial identity and white racial resentment, attitudes about the BLM movement on their own likely did not stand out as a distinct motivation for grievance or backlash among most white voters in 2016. Thus, we propose the following:

H1: Framing a candidate as a supporter of Black movements will not reduce support among Democratic-leaning white voters.

Due to the importance of these runoff elections, we also consider how voters may feel about the importance of a Democratic win in that context.

H2: Framing a candidate as a supporter of Black movements will make Democratic-leaning white voters see a Democratic victory as more important.

For Black respondents, we anticipate that framing around BLM would be a potent mobilizer (Bunyasi and Smith Reference Bunyasi and Smith2019; Bonilla and Tillery Reference Bonilla and Tillery2020; Tillery Reference Tillery2019). The behavior of Black voters in the 2016 cycle shows that the BLM movement holds the potential to stimulate positive gains for the Democratic Party in the polling booth. Building on Gillion’s (Reference Gillion2020) insights, we hypothesize that messaging about the BLM movement serves as a potent mobilizer for Black voters and liberal white voters to support the Democratic Party through canvassing, donations, and voting during the 2020 Senate runoff elections in Georgia.

We know from post-election analyses that the two Georgia Senate seats flipped because African American turnout surged and more college-educated, suburban whites voted for the Democrats in 2020 (Bullock Reference Bullock, Buchanan and Kapeluck2021). Because of a wealth of literature on racial appeals (see Stephens-Dougan Reference Stephens-Dougan2021), it seems clear that Black voters respond to authentic communications from candidates that speak to tangible issues directly affecting Black communities. Further, deracialized appeals tend to drive Black voters away from candidates (Stout 2020). BLM, specifically, is a particularly mobilizing focal point for Black political activism (Laird et al Reference Laird, Colquhoun and Jensen2025; Novick and Pickett Reference Novick and Pickett2024). As a result, we anticipate that Black voters in 2020 would be particularly responsive to appeals that specifically name BLM.

H3: Framing a candidate as an opponent of Black movements will make Black Americans more likely to mobilize in support of electing Democrat.

H4: Framing a candidate as an opponent of the Black movements will make Black Americans see a Democratic victory as more important to them.

Whereas Gillion’s (Reference Gillion2020) analyses were based on aggregate data from the American National Electon Study (ANES), we used two survey experiments during the height of the Georgia Senate runoff elections to discern how messaging that described politicians as either supportive of or opposed to the BLM and SHN movements impacted the willingness of our study subjects to canvass, donate, and vote for those candidates. There are several advantages to this approach. First, the experiments more accurately approximate the competitive messaging environment that existed in the national media during the Georgia runoff elections. Second, the experiments allow us to ensure that the subjects received direct messages about the BLM movement.

We investigate how racial justice frames, derived from the BLM and SHN movements, shape the behavior of Democratic and Democratic-leaning white respondents in Georgia and a national sample of Black respondents during Georgia’s 2020 Senate runoff elections. These runoff elections were critical for determining the partisan balance of power in the Senate and occurred amidst the second wave of BLM protests. We believe that framing politicians involved in the Senate runoff as either supporters or detractors of the BLM and SHN held the potential to move survey respondents in the direction of supporting the Democratic candidates for office through votes, donations, posting on social media, and volunteering at phone banks.

Experiment 1: White Respondents in Georgia

Study Description

First, we examine Hypotheses 1–2 through a survey experiment with white voters with moderate to liberal views in Georgia during the 2020 runoff elections for Senate. This test case is useful because it contains high external validity as public discussion focused on whether attention to BLM would cost Democrats the Senate (e.g., Martin and Herndon, Reference Martin and Herndon2020). We fielded the first study to a sample of 515 white Democratic voters, anticipating that this was the primary demographic who would be turned off by a Democrat-appeal to Black voters. We do not focus on the entire white Georgian population, because white Republicans are not a realistic sample to persuade (e.g., d’Urso, Bonilla, and Bogdanowicz Reference d’Urso, Bonilla and Bogdanowicz2025), with the assumption that these voters would not be targeted by Democrats nor open to messages by Democrats.Footnote 2 The survey was fielded through Cloud Research and was census-matched on gender and age between December 9 and 21, 2020.

First, we explained to each respondent that the runoff elections in Georgia could determine the majority party of the Senate if both Democratic candidates won. Respondents were then randomly sorted into one of five different treatment groups. The base message included an appeal from Democratic candidate Jon Ossoff to voters about what Ossoff would do in office.Footnote 3 For the baseline condition, we focus on a neutral issue where most citizens agree: civility in politics.Footnote 4 We include two positive controls, issues where voters would likely have more positive sentiments or increased importance: COVID policy and expanding the protections of the affordable care act (ACA).Footnote 5 These treatments speak to issues where Black Americans are disproportionately affected. Finally, we use two cases of Black mobilization efforts: BLM (addressing police violence) and SHN (addressing police violence with Breonna Taylor as an example).Footnote 6

Following the treatment, we asked respondents about their support for Jon Ossoff, a candidate for the runoff election in Georgia, the importance of a Democratic win, and if respondents believed the runoff votes would be fairly counted. We also asked a series of behavioral questions, which are reported in Appendix A. From our hypotheses, we predict that white Democratic-leaningFootnote 7 voters in Georgia who receive the BLM or SHN treatments will be more supportive of Jon Ossoff over the control condition (H1), and that this will increase the importance of a Democratic victory (H2).

Results

We analyze the data by using ordinary least squares regresson (OLS) to determine the effect of the treatments relative to the control condition. We recode each variable to range from 0 to 1, which means that each point can be read as a 100*β percent effect change caused by the treatments.Footnote 8 Relying on the assumptions around the randomization of experiences (Montgomery et al Reference Montgomery, Nyhan and Torres2018), we do not use controls. Demographic information, descriptive statistics, and balance tests are presented in Appendix A.Footnote 9 Due to the small sample and concern that the sample size could drive a null result, we also include power and effect size tests, which indicate that our results are consistent with a small to medium effect size.

Figure 1 presents our results. The BLM treatment does not have a statistically different effect from the control on any of the dependent variable measures. Though often studies are biased toward positive findings, we argue that this null finding is important because it suggests that Democrats do not suffer a net loss due to white dissatisfaction with the message when they focus on issues particularly relevant to Black communities. Indeed, we find that the BLM treatment has no statistically significant effect on white, Independent, and Democratic voters in Georgia. However, the treatment that saw the highest (positive) changes over the control was the SHN treatment. There is a positive increase in the likelihood of voting for Jon Ossoff (β = 0.08, p = 0.01). Neither the ACA nor the COVID treatments were statistically different from the control. Ultimately, through this experiment, we find mixed support for H1 and H2. Importantly, we also find no backlash affect among Democratic-leaning white voters in Georgia.

Figure 1. Results from white Democrat sample in Georgia.

Note: This figure plots the results of the OLS regression predicting the effect of the treatment conditions on the control (taxes) condition with robust standard errors. Each plot point represents the coefficient, and the bars represent the 95% confidence interval. The y-axis contains the various dependent variables and the x-axis represents the treatment effect compared to the control condition. Each scale was transformed to a 0–1 scale so that the plot points can be read as a 100*β percent change. A table of these results can be found in Appendix A.

Experiment 2: Black Respondents in Nationwide Sample

Study Description

We fielded the second study to a national sample of Black voters to answer the question of whether Democrats can bolster support outside their jurisdiction when they focus on appeals to Black audiences. This sample was fielded through Cloud Research and was census-matched on gender and age on November 23–29, 2020. The survey followed the same strategy as that of Study 1. However, Study 2 leveraged a national sample due to respondent availability, and as a result, modified focus to leadership of the Senate as a whole.Footnote 10 Because the national conversation was much more about the importance of a Democratic win for control of the Senate, we focused on the current Senate Majority Leader Mitch McConnell. As a result, the treatments feature negative language and describe McConnell’s opposition to Black movements.

Like our approach in Study 1, we explained the runoff elections, emphasizing that this election could determine the majority party of the Senate. Respondents were randomly sorted into one of five groups. Each message examined what McConnell would push for as Majority Leader if Republicans remained in the majority in the Senate following the runoff elections. We also include a slightly different baseline measure: one on lowering taxes, which follows typical Republican message, while the remaining messages remained the same: COVID, ACA, BLM, and SHN.Footnote 11 Following the treatment, we asked respondents about approval of McConnell, if votes would be fairly counted, and the importance of a Democratic win.Footnote 12 We hypothesize that the Black respondents who receive treatments about BLM or SHN will be less supportive of McConnell (i.e., more supportive of Democrats, H3) and that they will think a Democratic win is more important (H4). We also include similar political behavior questions as in Study 1, presented in Appendix B.

Results

We follow the same analytic procedures as in Study 1. We also include descriptive statistics for the sample and dependent variables, as well as balance tests, regression tables, and results with demographic covariates in Appendix B.

Figure 2 presents these results. The point estimate represents the OLS coefficient, and the error bar shows the 95% confidence interval. Approval for McConnell decreases as a result of all treatments, but significantly so for the BLM (β = −0.08, p = 0.01), ACA (β = −0.08, p = 0.01), and COVID treatments (β = −0.07, p = 0.04). The importance of Democrats to win the election increases due to the BLM treatment (β = 0.04, p = 0.04). Respondents were no more likely to support Democratic candidates after seeing the SHN treatment for any dependent variable, however.

Figure 2. Results of black national sample.

Note: This figure plots the results of the OLS regression predicting the effect of the treatment conditions against the baseline (taxes) with robust standard errors. Each plot point represents the coefficient, and the bars represent the 95% confidence interval. The y-axis contains the various dependent variables and the x-axis represents the treatment effect compared to the control condition. Each scale was transformed to a 0–1 scale so that the plot points can be read as a 100*β percent change. A table of these results can be found in Appendix B.

Respondents were more likely to respond to messages explicitly supporting Black movements compared to the control condition. These findings suggest that Black voters are more likely to be responsive if they see messaging that speaks to the disparities that Black Americans face as a result of policy (H3 and H4). Importantly, respondents were also more responsive to messages on COVID and (to a lesser extent) the ACA, as well. This suggests that Black respondents were able to connect issues disproportionately affecting their communities, even without the mention of a social movement. Ultimately, these results suggest that messages likely to mobilize Black communities are those that speak directly to Black community’s interests as a whole.

Discussion

In this paper, we experimentally test whether messages around BLM and SHN created a backlash effect among white voters, while also mobilizing Black voters. We use the 2020 Georgia runoff election to frame our experiments because the election took place during one of the largest mobilization periods of BLM and was pivotal for Democratic control of the Senate.

We find that Democratic-leaning white respondents were equally likely to support the Democratic candidate with a message about Black social movements as they were about other social issues. For Black voters, there was significantly more opposition to Republican leadership when they opposed Black social movements than the baseline statement, and more consistent opposition to messages about Black social movements than other issues disproportionately affecting Black communities. While there continues to be debate about whether support for BLM (and identity politics writ large) has created more of a backlash among center-left and centrist Americans, this paper provides some evidence that at least in 2020, BLM served as a potent message for mobilizing Black communities while not demobilizing white communities. We demonstrate a lack of pushback for candidates supporting Black appeals.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/rep.2025.10042

Acknowledgments

The authors wish to thank the Center for the Study of Diversity and Democracy at Northwestern University for funding for this project. We also thank the anonymous reviewers and the editorial team for their feedback and comments which greatly improved and streamlined our argument.

Footnotes

1 By Democrat-leaning white voters, we consider any white American who would answer the 7-point party identification scale with Independent to Strong Democrat. We use the term “Democrat-leaning” or “left-leaning” for ease in the text, and as a way to limit the scope of this paper to voters who might feasibly vote for a Democrat in any election. The intent of this strategy is to narrow our question and inquiry to examine the pundit questions that we outlined above.

2 We anticipate that messages supporting Black movements would have a strong negative effect on conservative white Democrats (Hutchings and Jardina Reference Hutchings and Jardina2009, Stephens-Dougan Reference Stephens-Dougan2020, Bonilla et al Reference Bonilla, Filindra and Lajevardi2022). While interesting to document, it is outside the scope of our question. More practicality, including this consideration, would double our sample because it would require a moderation in analysis. We believe that the feasibility (and expense) of doing so, combined with the scope of the project, means that this additional measurement is unnecessary.

3 While Raphael Warnock was also running at the same time, Jon Ossoff’s race seemed more contentious because he ran against a high-profile candidate, incumbent Republican Senator David Perdue. We focused on the race that would yield the most difficult test of our theory.

4 We include a baseline condition following others rather than no message to minimize differences between control and treatment groups and because we believe this allows a similar amount of information to be displayed for all groups.

5 After four years of Democratic leadership in the White House and a turn of public opinion on the ACA, attacks on the ACA have substantially decreased. However, in 2020, eliminating the ACA was still a standard Republican claim.

6 See Appendix Table A.1 for the complete treatment wording.

7 We sampled for Democratic white voters and removed anyone from the resulting sample that indicated they were a pure Independent or leaned Republican.

8 Note, this transformation is not to a binary scale; if a scale were five points, it would be recoded 0, 0.25, 0.50, 0.75, 1.

9 Because some demographic variables yielded imbalance, we also present results with controls in the appendix. This does not change our overall conclusions.

10 We understand and agree that a sample composed of Black Georgians would be the ideal comparison. However, the vendor was concerned about the representativeness of a Black sample. Thus, we leveraged the fact that Black Americans are uniquely high in their agreement on politics to proxy Black Georgian attitudes with a national Black sample.

11 Appendix Table B.1 has the full treatment messages.

12 Importantly, because this sample lives throughout the United States, we cannot use the equivalent measure of vote, so we instead measure approval for McConnell.

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Figure 0

Figure 1. Results from white Democrat sample in Georgia.Note: This figure plots the results of the OLS regression predicting the effect of the treatment conditions on the control (taxes) condition with robust standard errors. Each plot point represents the coefficient, and the bars represent the 95% confidence interval. The y-axis contains the various dependent variables and the x-axis represents the treatment effect compared to the control condition. Each scale was transformed to a 0–1 scale so that the plot points can be read as a 100*β percent change. A table of these results can be found in Appendix A.

Figure 1

Figure 2. Results of black national sample.Note: This figure plots the results of the OLS regression predicting the effect of the treatment conditions against the baseline (taxes) with robust standard errors. Each plot point represents the coefficient, and the bars represent the 95% confidence interval. The y-axis contains the various dependent variables and the x-axis represents the treatment effect compared to the control condition. Each scale was transformed to a 0–1 scale so that the plot points can be read as a 100*β percent change. A table of these results can be found in Appendix B.

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