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Can locally-targeted feedback encourage the use of COVID contact tracing apps? Control Experiment

Last registered on May 24, 2021


Trial Information

General Information

Can locally-targeted feedback encourage the use of COVID contact tracing apps? Control Experiment
Initial registration date
May 24, 2021

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
May 24, 2021, 8:54 AM EDT

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



Primary Investigator

University of Oxford

Other Primary Investigator(s)

PI Affiliation
University of Bonn, National University of Singapore
PI Affiliation
National University of Singapore

Additional Trial Information

In development
Start date
End date
Secondary IDs
One important tool in the controlling of the COVID-19 pandemic lies in fast and effective detection of individuals who have been in close contact with infected persons. In this context, contact tracing apps such as the German "Corona-Warn-App" (CWA) can play an important role.
Installation and use of the contact tracing app represents a public good: everyone, even those who do not install an app, benefits from faster tracking of suspicious cases. Recent updates to the functionality of the CWA allow for significantly enhanced tracing effectiveness by introducing a Check-in function as well as reporting of rapid antigen test results.

In our main experiment (AEARCTR-0006529), we investigated whether and how targeted feedback on local COVID-19 incidence rates and social comparisons with other regions can increase the willingness to install the CWA. We tested this on a large-scale through targeted social media ads. One interesting finding was that the increases in click-through rates were to a significant part predicted by incrases in initial level of attention. In this control experiment, we aim to further examine the behavioral mechanisms underlying the results from our main experiment. In particular, we plan to test modifications of our original interventions to study the roles of information targeting and perceptual salience.
External Link(s)

Registration Citation

Chen, Zihua, Ximeng Fang and Lorenz Goette. 2021. "Can locally-targeted feedback encourage the use of COVID contact tracing apps? Control Experiment." AEA RCT Registry. May 24.
Experimental Details


For our main interventions, we deliver ads on Facebook that are targeted on county/city level and provide feedback on the local incidence rates as well as a comparison with other counties/cities in Germany.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Click-through rate of the Facebook ads, which link to the official homepage of the Corona-Warn-App
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Views of ad video as intermediary outcome
Secondary Outcomes (explanation)
Treatment could in essence affect click rates through both the extensive margin (engaging with the ad) and the intensive margin (response to ad content). Video view metrics can help us disentangle the two, at least if selection effects are limited.

Experimental Design

Experimental Design
Video ads on Facebook:
1) Control group: conventional ad for CWA (from actual Marketing campaign), includes info slogan about effectiveness of ad in stopping infection chains
2) Targeted feedback + Salience: feedback on local incidence rate on comparison with rest of state; the video makes the comparison salient and adds an injunctive norm
3) Targeted feedback (no salience): like Treatment 2, but no salient comparison frames
4) Non-targeted feedback: like Treatment 3, but feedback on overall German incidence rate
Experimental Design Details
Randomization Method
Randomization through Facebook's A/B testing functionalitiy
Randomization Unit
Randomization on individual user level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Aim for about 1 million impressions on Facebook
Sample size: planned number of observations
same as clusters
Sample size (or number of clusters) by treatment arms
Roughly equal number of observations in each treatment condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
July 30, 2021, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
July 30, 2021, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
1 million
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
1 million
Final Sample Size (or Number of Clusters) by Treatment Arms
about 250k per treatment arm
Data Publication

Data Publication

Is public data available?

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials