Elite Cues and Mass Non-Compliance

European Political Science Association 13th Annual Conference

Zachary P Dickson & Sara B Hobolt

London School of Economics

Motivation

Background – what do we already know?

Elites cues shape policy views Zaller and Feldman (1992)
Elite cues inflame partisan polarization, increasing support for political violence (Armaly, Buckley, and Enders 2022)
Elite cues caused partisan differences in public health behavior during the pandemic
- Grossman et al. (2020) show that US state governors’ were more effective at motivating social distancing behavior in Democratic-leaning counties than Republican-leaning counties
- Bisbee and Lee (2022) show that reductive messages from President Trump play a similar role as objective information (COVID-19 cases/deaths) in influencing social distancing behavior
Limitations?
- Existing research simply observes differences in partisan behavior

Trump’s targeted tweets

We leverage the fact that Trump called for the “liberation” of three specific states (MN, MI & VA) on April 17, 2020

The context

Did the public respond?

_{Note: Google Trends data are normalized and scaled according to time period and geography in order to represent the relative popularity of a search term on a range between 0 and 100 (Google 2020).}

Did the public respond?

How were the messages received?

_{Note: Topic models include all quote tweets (143,171) of Trump’s LIBERATE tweets. A detailed description of text pre-processing and modeling methods are available in Appendix A.}

Research Design

Generalized Difference-in-Differences
- Estimand = targeted cue
  - Mean difference between targeted/non-targeted states (county level)
Treatment & control groups
- Targeted [non-targeted] counties
Multiple models & counterfactuals
- Targeted Republican [Democratic] counties vs non-targeted Republican [Democratic] counties

Data

Two forms of mobility data measured daily at the county level
- Meta mobility data (Meta 2022)
  - Stay-at-home compliance & Mobility
- Google mobility data (Aktay et al. 2020)
  - Recreational & Aggregated mobility
County-level data on COVID-19 cases and deaths

Results – Mobility

Table 1: Cummulative estimates: Mobility

(a) *DV: Mobility*
	Full State	Democratic Counties	Republican Counties
Treatment	2.284*	1.005	2.706**
	(0.906)	(0.631)	(0.854)
R2	0.764	0.825	0.714

(b) *DV: Stay-at-home Compliance*
Full State	Democratic Counties	Republican Counties
-1.128*	-0.660	-1.336**
(0.502)	(0.438)	(0.476)
0.883	0.904	0.869

Note : + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

_{Estimates are from two-way fixed effects models with county and time fixed effects. Standard errors are clustered by state and time. See Appendix D in paper for full results.}

Dynamic Results – Mobility

Event Study Estimates for the effect of Trump’s calls for Liberation on Mobility in Red counties.

_{Note: April 17th is Day 1. Full results are presented in Appendix D}

Spillover effects – Crime

Identify crimes related to sentiment expressed in analysis of quote-tweets of Trump’s tweets
- Daily statewide arrests for Disorderly conduct; Assault (Aggravated and Simple); Destruction/Damage/Vandalism of Property
20-day window around Trump’s tweets (+/-10 days)
Racial heterogeneity in succeptibility to cues
- 6% of black voters and 28% of Hispanic voters supported Trump in 2016
- 54% of whites, including 62% of white men
Counterfactual: statewide arrests of whites for the same crimes in non-targeted states

Results – Crime

Substantive effects:

\(e^{0.121} - 1 \approx 12.7\%\) increase in arrest rate of whites in states where Trump called for liberation
483 total arrests in the treatment group
\(483/e^{0.121} \approx 55\) additional arrests of whites in states where Trump called for liberation

*ATT: Arrest rate/100k*
	Estimate	S.E.	CI.lower	CI.upper	p.value
ATT.avg	0.121	0.054	0.015	0.226	0.025

Robustness

Exogeneity
Excludability
Mobility
- Alternative measure of mobility – Google mobility data (Appendix E)
  - Retail & recreation, and Aggregate mobility
- Alternative estimation strategy – first-difference (Appendix F)
Crime
- No effect of cues on arrest rate of other races (Appendix I)
- No effect of cues on arrest rate of entire state population (Appendix J)
- Alternative measurement of arrests – Two-day moving average (Appendix K)
- Alternative modeling strategy – TWFE with state & date fixed effects (Appendix K)

Discussion & Concluding Remarks

Elites can motivate both compliance and non-compliant and even violent criminal behavior
- Trump’s calls for liberation led to an increase in non-compliant behavior
Final considerations
- Hard test - estimates are conservative given counterfactuals
- Is the American case unique?

Thank you!

References

Aktay, Ahmet, Shailesh Bavadekar, Gwen Cossoul, John Davis, Damien Desfontaines, Alex Fabrikant, Evgeniy Gabrilovich, et al. 2020. “Google COVID-19 Community Mobility Reports: Anonymization Process Description (Version 1.1).” arXiv Preprint arXiv:2004.04145.

Armaly, Miles T, David T Buckley, and Adam M Enders. 2022. “Christian Nationalism and Political Violence: Victimhood, Racial Identity, Conspiracy, and Support for the Capitol Attacks.” Political Behavior 44 (2): 937–60.

Bisbee, James, and Diana Da In Lee. 2022. “Objective Facts and Elite Cues: Partisan Responses to Covid-19.” The Journal of Politics 84 (3): 1278–91.

Broockman, David E, and Daniel M Butler. 2017. “The Causal Effects of Elite Position-Taking on Voter Attitudes: Field Experiments with Elite Communication.” American Journal of Political Science 61 (1): 208–21.

Google. 2020. “Google Trends: Search Term: “Liberate",” April. https://trends.google.com/trends/explore.

Grossman, Guy, Soojong Kim, Jonah M Rexer, and Harsha Thirumurthy. 2020. “Political Partisanship Influences Behavioral Responses to Governors’ Recommendations for COVID-19 Prevention in the United States.” Proceedings of the National Academy of Sciences 117 (39): 24144–53.

Meta. 2022. “Movement Range Maps.” https://data.humdata.org/dataset/movement-range-maps.

Tappin, Ben M. 2023. “Estimating the Between-Issue Variation in Party Elite Cue Effects.” Public Opinion Quarterly 86 (4): 862–85. https://doi.org/10.1093/poq/nfac052.

Zaller, John, and Stanley Feldman. 1992. “A Simple Theory of the Survey Response: Answering Questions Versus Revealing Preferences.” American Journal of Political Science 36 (3): 579–616. https://doi.org/10.2307/2111583.