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Incentivizing Data Donations: Can Monetary Compensation Increase Data Contributions?
Initial registration date
July 13, 2020
July 13, 2020 2:36 PM EDT
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University of Passau
Other Primary Investigator(s)
University of Passau
Additional Trial Information
Today, individuals commonly disclose personal data to enjoy the benefits of data-driven services, such as personalized user interfaces and targeted content recommendations. Next to these personal benefits, data from individuals can also create great societal returns in the public interest. In this spirit, several countries have introduced mobile tracking apps in response to the COVID 19-pandemic, to facilitate contact tracing based on the continuous collection of users’ contact data with others. However, such tracking apps can only represent effective building blocks for a nation’s public health strategy if individuals are willing to voluntarily donate their data by installing and using these apps. Despite the potential societal benefits, empirical research on data donations is still scarce. In particular, it is unknown which mechanism can be effective in encouraging individuals to donate their data in the public interest and whether monetary payments can increase the willingness to contribute data. Previous studies on other types of donations, such as blood donations, show that monetary compensation can crowd out intrinsic motivation and altruistic motives, and thus, reduce the number of blood donors. However, in the context of data, monetary compensation could provide a short-term stimulus that may foster long-term data donations. To address this empirical research question, we run an experimental study and compare the effect of different incentives on participants’ willingness to donate data. Altogether, our findings provide timely evidence on how to encourage data donations in the interest of public health.
Fast, Victoria and Daniel Schnurr. 2020. "Incentivizing Data Donations: Can Monetary Compensation Increase Data Contributions?." AEA RCT Registry. July 13.
We conduct an online experiment and elicit subjects’ (revealed) willingness to install and use the Corona-Warn-App that is provided by the German federal government. We vary the type of incentive offered to the subjects for the installation and usage of the app.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Subjects’ decision to install the Corona-Warn-App on their own smartphone (verified by experimenter).
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Subjects’ revealed use of the Corona-Warn-App (verified 14 days after installation).
Secondary Outcomes (explanation)
Experiments are run online. Treatments are randomized at the session level. Participants will be recruited from the student subject pool of the University of Passau. Each subject participates in only one treatment (between-subject design). In all treatments, subjects are fully informed about the timeline of the experiment and the consequences of their actions.
Experimental Design Details
Randomization by computer in office
Was the treatment clustered?
Sample size: planned number of clusters
Observations on the subject level are assumed to be independent, because subjects decide only once on app installation and decide without interacting with other participants in the experimental session. Thus, the number of clusters equals the number of observations.
Sample size: planned number of observations
We schedule data collection aiming at 90 observations per treatment. Thus, we aim for a total of 360 individual participants across the four treatments.
Sample size (or number of clusters) by treatment arms
90 (student) participants per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
INSTITUTIONAL REVIEW BOARDS (IRBs)
German Association for Experimental Economic Research e.V.
IRB Approval Date
IRB Approval Number