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Can Civil Society Organisations Sway Elections? – Field Experiment
Last registered on July 19, 2020

Pre-Trial

Trial Information
General Information
Title
Can Civil Society Organisations Sway Elections? – Field Experiment
RCT ID
AEARCTR-0004622
Initial registration date
August 24, 2019
Last updated
July 19, 2020 5:01 AM EDT
Location(s)

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Primary Investigator
Affiliation
LMU Munich
Other Primary Investigator(s)
PI Affiliation
LMU Munich
Additional Trial Information
Status
In development
Start date
2019-08-24
End date
2021-02-01
Secondary IDs
Abstract
This document describes the pre-analysis plan for a field experiment evaluating the effective-ness of civil society campaigns in shaping election outcomes. This field experiment will be embedded in the Facebook advertising campaign of a German civil society organization dur- ing the run-up to three state elections in Germany in autumn of 2019. The main objective of this field experiment is to test the hypothesis whether civil-society organization can shape elec- tion outcomes via online campaigns. To pursue this objective, we design a randomized field experiment which experimentally varies which postal districts in these three states are exposed to the organization’s Facebook campaign and which are not. In this document we describe the motivation for conducting this experiment, the data and experimental design as well as our main hypotheses and the corresponding empirical analyses.
External Link(s)
Registration Citation
Citation
Vollmer, Leonhard and Johannes Wimmer. 2020. "Can Civil Society Organisations Sway Elections? – Field Experiment." AEA RCT Registry. July 19. https://doi.org/10.1257/rct.4622-1.2000000000000002.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-08-24
Intervention End Date
2019-10-27
Primary Outcomes
Primary Outcomes (end points)
Our main outcomes are election outcomes taken from official sources. We consider the following two primary outcomes:
1. Vote share of "Alternative für Deutschland" in the state elections in Brandenburg, Sachsen, and Thüringen in autumn 2019.
1. Turnout in the state elections in Brandenburg, Sachsen, and Thüringen in autumn 2019.

For more details on the outcomes employed in our study please see our pre-analysis plan.
Primary Outcomes (explanation)
We take our primary outcomes directly from official statistics. The only transformation we conduct is aggregation to the postal district level.
Secondary Outcomes
Secondary Outcomes (end points)
Besides our main outcomes, we employ a set of additional outcomes:
1. social media data (e.g. posts by location and sender)
2. further political outcomes (e.g. demonstrations)
3. internal data from our project partner (e.g. performance metrics of Facebook campaign)

For more details on our set of additional outcomes please refer to our pre-analysis plan.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We design a randomized field experiment which experimentally varies which postal districts in the three states of Brandenburg, Sachsen, and Thüringen are exposed to the Facebook campaign of our project partner. Our experimental design has the following key features: first, postal districts constitute the level of randomization. Second, we employ a stratified design as to reduce differences in pre-treatment characteristics between postal districts in the treatment and the control group. To be precise, we construct strata of four postal districts which are similar with respect to their past election outcomes as well as population. Within each of these strata we assign 50 percent of postal districts to the treatment group and the remaining 50 percent to the control group.

We specify further details of our design in our pre-analysis plan.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer (DeclareDesign package for R)
Randomization Unit
Postal districts
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
812
Sample size: planned number of observations
In case of the cluster-level analysis (postal districts): 812 In case of the unit-level analysis (electoral-precincts): ca. 9500 (we will know the exact number only once the official election results are available because the number of electoral precincts varies over time)
Sample size (or number of clusters) by treatment arms
406
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations yield that we are able detect treatment effects starting at approximately 3 percent of a standard deviation
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Ethics Commission, Department of Economics, University of Munich
IRB Approval Date
2019-06-26
IRB Approval Number
2019-13
Analysis Plan

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