Beliefs on shadow profiling on support for government intervention (Pilot)

Last registered on December 06, 2023

Pre-Trial

Trial Information

General Information

Title
Beliefs on shadow profiling on support for government intervention (Pilot)
RCT ID
AEARCTR-0012516
Initial registration date
November 30, 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 06, 2023, 8:28 AM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Bremen

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-11-30
End date
2023-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study explores the causal relationship between individuals' beliefs about shadow profiling—an online platform's ability to collect data without explicit consent—and their support for government-led data privacy initiatives. Information in shadow profiles can be collected both from active users, who did not provide the information, and from non-users, who do not use the service. In this study, I will collect data from a sample of U.S. adults using an online information provision experiment. Initially, I will elicit participants' beliefs about the online data collection practices of online platforms, with a focus on shadow profiling. Based on the study by Aguiar et al. (2022), half of the participants will then be provided with detailed information about the potential ability of online services to create shadow profiles, while the other half, serving as the control group, will not receive this information. Aguiar et al. (2022) examine the ability of online platforms to accumulate data about internet users without obtaining their permission, thereby specifically focusing on Facebook. Their findings suggest that Facebook is able to track about 40% of internet browsing activities. The overlap in browsing behavior between users and non-users potentially enables the platform to make accurate predictions of personal information, which can then be monetized for advertising purposes. While the study by Aguiar et al. (2022) primarily focuses on Facebook, the concepts and methods of shadow profiling are applicable to any online platform that utilizes similar data collection tools and techniques. After participants have been exposed to this evidence, I will assess all respondents' support for different government interventions aimed at mitigating the ability of online platforms to build shadow profiles.

Aguiar, L., Peukert, C., Schäfer, M., & Ullrich, H. (2022). Facebook shadow profiles. arXiv preprint arXiv:2202.04131.
External Link(s)

Registration Citation

Citation
Stenzhorn, Eliza. 2023. "Beliefs on shadow profiling on support for government intervention (Pilot)." AEA RCT Registry. December 06. https://doi.org/10.1257/rct.12516-1.0
Experimental Details

Interventions

Intervention(s)
Please refer to the pre-analysis plan for further details.
Intervention Start Date
2023-11-30
Intervention End Date
2023-12-31

Primary Outcomes

Primary Outcomes (end points)
Please refer to the pre-analysis plan for further details.
Primary Outcomes (explanation)
Please refer to the pre-analysis plan for further details.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Please refer to the pre-analysis plan for further details.
Experimental Design Details
I conduct a randomized information provision experiment with a sample of U.S. adults. The experiment is divided into a control group and a treatment group. Initially, I elicit participants' beliefs about online data collection practices, with a focus on shadow profiling. Following this, the treatment group receives detailed information about shadow profiling, based on the findings of a recent study by Aguiar et al. (2022). In contrast, the control group does not receive any information. After the intervention, I assess all participants' support for government interventions aimed at mitigating the ability of platforms to build shadow profiles. For more comprehensive details, please refer to the pre-analysis plan.
Randomization Method
Participants will be randomly assigned to either the treatment or control groups using a designated randomization function within the Unipark software package.
Randomization Unit
Randomization will be conducted at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
The study does not involve clustering, as randomization occurs at the individual level. Therefore, the number of clusters is equivalent to the total number of individual participants in the sample.
Sample size: planned number of observations
I plan to recruit 1,000 U.S. citizens of at least 18 years of age as individual participants for this study, utilizing the Prolific Academic platform for recruitment.
Sample size (or number of clusters) by treatment arms
Treatment group: 500 individuals
Control group: 500 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Please refer to the pre-analysis plan for further details.
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2023-11-29
IRB Approval Number
G4BEBv93
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials