Populism, Information, and Network Effects

Last registered on December 01, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Populism, Information, and Network Effects

RCT ID

AEARCTR-0012504

Initial registration date

November 16, 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

December 01, 2023, 4:20 AM EST

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

2023-11-17

End date

2023-12-27

Keywords

Behavior, Governance, Other

Additional Keywords

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

JEL code(s)

P00

Secondary IDs

Prior work

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

Abstract

This study follows up on a study we just conducted during the 1st round of Argentina Presidential Election on October 22, 2023, which was called "Fighting Populist Misinformation with a Leaflet Campaign". The purpose of the present study is twofold. First, given that Javier Milei made it (together with Sergio Massa) to the 2nd round of the election, this gives us a unique opportunity (rarely available in information treatment experiments as such opportunities are usually unique) to replicate the earlier experiment and check whether it replicates. Second, the preliminary data from the first experiment suggest quite strong network effects - which we thought were likely and planned to measure (and preregistered). Given that, we use the 2nd round as an opportunity to more clearly decompose direct effects from indirect (network) effects. We therefore work with the NGO to send leaflets to a subsample of voters who are supporters of the PJ party and who were in "pure control" departments in the previous ones and were therefore unaffected by our earlier work. These leaflets would provide information about Milei's policy proposals and their consequences. We will then use the official election data (precinct ("mesa")-level) to measure both the direct effect of our campaign on treated voters and the voters treated indirectly through informational spillover / network effects, with the exact proposal for measurement in the pre-analysis plan.

External Link(s)

Registration Citation

Citation

Egorov, Georgy et al. 2023. "Populism, Information, and Network Effects." AEA RCT Registry. December 01. https://doi.org/10.1257/rct.12504-1.0

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Experimental Details

Interventions

Intervention(s)

Information leaflets containing accurate information debunking the false claims of a right-wing populist candidate are set to a subset of voters. We will use precinct-level election results and the intensity of treatment to measure the effectiveness of this information campaign both in terms of the direct effect and spillovers.

Intervention Start Date

2023-11-17

Intervention End Date

2023-11-25

Primary Outcomes

Primary Outcomes (end points)

We will look at election outcomes at mesa level in treated departments, in both treated and untreated mesas in those departments, and compare them to mesas in control departments to infer both the direct and indirect effect.

Primary Outcomes (explanation)

Consider a polling location, such as school. It contains several mesas, some are of which are not treated, some are treated, and the treated mesas may vary by the number/share of voters treated just because of data availability. We seek to measure direct effect of treatment (on those treated) and indirect effect (on those voting in the polling location by not treated). We do it the following way.

While by looking at the "not treated" mesas we can get an idea about indirect effects, in a situation where indirect effects can be strong, looking just at the results in treated mesas would be misleading, because these mesas typically include some individuals treated directly and many more individuals not treated directly (but likely treated indirectly). We therefore construct the following variables.

The mesa-level contribution of the direct treatment effect is proportional to the share of individuals treated in a given mesa, which is zero for untreated mesa and a positive value for treated ones. So this is our "direct" variable.

The indirect treatment effect should be thought of as follows. The locality where people live defines the polling place, but people are assigned to mesas by the last name. This means that indirect effect that treated individuals impose on untreated ones (and even on other treated ones) should be roughly the same regardless of whether they happen to vote in the same mesa or different ones ("roughly" is because last names are not randomly assigned, and two individuals with the same last name are more likely to be e.g. father and son than those with different last names, but these differences are likely small). Consequently, an individual's exposure to indirect treatment effect should be proportional to the share of treated individuals in the given locality - i.e., among individuals in the same polling place. So, for our measure of indirect effect we use the share of people treated at the polling location / "school" level. (This measure may be possible to adjust if one believes that directly treated individuals are immune to indirect effects, but we assume that the indirect effect works uniformly, for example, because treated individuals do not know whether a person they are trying to convince was treated or not.)

Notice that our randomization of the share of mesas we treat in a particular polling location allows us to avoid the multicollinearity issue and identify the direct and indirect effects independently.

We will also 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)

Secondary Outcomes (explanation)

Experimental Design

We provide truthful information from reputable about a populist candidate's policy proposal and its consequences to see how this affects voting decisions, to treated voters (direct effect) and to those living nearby (indirect effect).

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 and 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.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

We are treating mesas in 50 polling locations overall, plus there are paired mesas in the control departments, spanning 56 polling locations. This gives 106 clusters (polling locations, typically schools).

Sample size: planned number of observations

We are treating 154 mesas in 50 polling locations and not treating 109 mesas in those locations. The number of control mesas used is 157. Thus, we will have up to 154+109+157 = 420 observations.

Sample size (or number of clusters) by treatment arms

154 treated mesas in 50 treated polling locations, 109 untreated mesas in the same 50 locations, 157 untreated mesas in 56 untreated locations.

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

Supporting Documents and Materials

IRB