Flighting Populist Misinformation with a Leaflet Campaign

Last registered on November 01, 2023

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Pre-Trial

Trial Information

General Information

Title

Flighting Populist Misinformation with a Leaflet Campaign

RCT ID

AEARCTR-0012305

Initial registration date

October 19, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

November 01, 2023, 2:31 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Argentina

Region

Salta

Primary Investigator

Name

Ekaterina Zhuravskaya

Affiliation

Paris School of Economics

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Maxim Mironov

PI Affiliation

IE Business School

Contact Investigator

PI Name

Sergei Guriev

PI Affiliation

Sciences Po

Contact Investigator

PI Name

Georgy Egorov

PI Affiliation

Kellogg School of Management

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-10-20

End date

2023-12-27

Keywords

Behavior, Governance, Other

Additional Keywords

Populism, Leaflets, Misinformation, Information Campaign, Elections, Argentina, Milei

JEL code(s)

P00

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We study the electoral effects of informational leaflets that debunk false claims made by a right-wing populist candidate. Following Javier Milei's unexpected victory in the zero-round elections (Las Primarias, Abiertas, Simultáneas y Obligatorias, PASO) in Argentina in August 2023, a non-partisan NGO embarked on a project aimed at informing voters in one of the poorest provinces in Argentina before the first round of elections that the claims Milei made during his election campaign are false and that his promises are unattainable. We collaborated with this NGO to construct a random sample of pairs of electoral precincts with very close zero-round results. We then randomized precincts within each pair into treatment and control precincts. During the election campaign leading up to the first round of the elections, the NGO sends out leaflets by regular mail containing accurate information that corrects false claims of the populist front-runner. These leaflets were distributed to approximately 10% of voters in the treatment precincts in a random subset of the pairs. The mailing by the NGO is done using information voluntarily provided by these voters on a publicly available online resource of the supporters of one of the major centrist political parties, PJ. Many supporters of this party are anticipated to switch their vote to the populist candidate, Milei. We will use the official data on the first and second-round election results at the precinct level to evaluate the effects of this information campaign on voting and estimate the informational spillovers between neighboring precincts.

External Link(s)

Registration Citation

Citation

Egorov, Georgy et al. 2023. "Flighting Populist Misinformation with a Leaflet Campaign." AEA RCT Registry. November 01. https://doi.org/10.1257/rct.12305-1.0

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Experimental Details

Interventions

Intervention(s)

Two sets of informational leaflets containing accurate information debunking the false claims of a right-wing populist candidate are sent to two non-overlapping subset of voters. We will use precinct-level voting outcomes, as compared to a control group, to measure the effectiveness of these informational campaigns.

Intervention Start Date

2023-10-20

Intervention End Date

2023-11-29

Primary Outcomes

Primary Outcomes (end points)

Election results at the minimal level they are reported (mesas) in treated and control mesas.

Primary Outcomes (explanation)

We will compare the election outcomes in treated and control mesas to measure the impact of each of our two treatments, inflation and education. We will examine the balance between treated and control mesas across the available set of covariates and will control for misbalanced pre-treatment covariates.

Secondary Outcomes

Secondary Outcomes (end points)

Election results at mesa level in untreated mesas that share their polling location with treated mesas and control mesas

Secondary Outcomes (explanation)

We seek to measure network effects / spillovers from our treatment by looking at mesas that we did not treat but that are in the same location as the mesas that we did treat (and thus the voters live close together and know each other and could tell each other the information we provided - and comparing these outcomes to the control mesas where we do not have any intervention. As with primary outcome, we will control for misbalanced pre-treatment covariates.

Experimental Design

We provide truthful information from reputable about a populist candidate's policy proposals and their consequences to see how this affects voting decisions.

Experimental Design Details

See "intervention" above

Randomization Method

Randomization is done in office by a computer. We set a seed to make randomization replicable.

Randomization Unit

We randomize polling locations (typically schools) into control, inflation treatment and education treatment. Within the latter two groups, we randomized the intensity of treatment (the share of mesas to treat in each such group) subject to our goal of sending ~5,000 leaflets in each of the treatments.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

In total, we are going to treat 328 mesas (84 precincts), 163 with inflation treatment (in 42 precincts) and 165 with educational treatment (42 precincts)
In addition we are going to estimate the network effect for 105 pairs for inflation treatments (in 35 precincts) and 101 pairs for the educational treatment (in 35 precincts)

Sample size: planned number of observations

In total, we are going to treat 328 mesas (84 precincts), 163 with inflation treatment (in 42 precincts) and 165 with educational treatment (42 precincts) In addition we are going to estimate the network effect for 105 pairs for inflation treatments (in 35 precincts) and 101 pairs for the educational treatment (in 35 precincts)

Sample size (or number of clusters) by treatment arms

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB