Eliciting the Willingness to Sell Data from Multiple Social Networks

Last registered on August 23, 2023


Trial Information

General Information

Eliciting the Willingness to Sell Data from Multiple Social Networks
Initial registration date
January 18, 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
January 23, 2023, 7:03 AM EST

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

Last updated
August 23, 2023, 4:24 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

University of Siegen

Other Primary Investigator(s)

PI Affiliation
Düsseldorf Institute for Competition Economics - Heinrich Heine University Düsseldorf
PI Affiliation
Düsseldorf Institute for Competition Economics - Heinrich Heine University Düsseldorf
PI Affiliation
Düsseldorf Institute for Competition Economics - Heinrich Heine University DüsseldorfDüsseldorf Institute for Competition Economics - Heinrich Heine University Düsseldorf

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We experimentally investigate the willingness to sell highly sensitive personal data from different social networks. To analyze the perceived loss of selling data, we have sequential selling decisions in which we vary between researchers and external firms as buyers, and we vary the number of buyers. In particular, we are interested in (i) whether our participants are more willing to sell additional data sets if they have already sold data to the same buyer, (ii) whether the willingness to sell differs depending on the type of buyer, and (iii) whether the willingness to sell differs for alternating buyers. We link our analysis to current debates in competition economics. We further collect information about personality traits to investigate sellers’ heterogeneity.
External Link(s)

Registration Citation

Döpper, Hendrik et al. 2023. "Eliciting the Willingness to Sell Data from Multiple Social Networks." AEA RCT Registry. August 23. https://doi.org/10.1257/rct.10791-4.0
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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcome of interest will be the monetary value individuals assign to their network data for a certain network-buyer combination.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experiment comprises four stages incorporating a between-subject design. Each participant plays only once. The experiment is implemented using oTree (Chen et al., 2016) and conducted online. The data collecting service TGM Research recruits our participants. Participants receive detailed instructions in the beginning of the experiment.
Experimental Design Details
Simulation round of BDM procedure
Participants have the opportunity to practice the sequential Becker-DeGroot-Marschak (1964; hereafter BDM) selling mechanism described in stage 2. They are confronted with hypothetical sequential sales decisions on their place of residence, postal code and name and have the opportunity to repeat this three-step procedure. They obtain direct feedback on their decisions, which enhances their understanding of the BDM mechanism and ensures conscious decisions in stage 1.
After each simulation round, participants can decide to leave the simulation and continue with the experiment. We explicitly state that subjects should only proceed if they understand the mechanism and that we are available to answer questions via e-mail.

Stage 1a: Determining willingness to sell social media data
Questionnaire on platform affiliation
In this stage, participants reveal what social networks they use.

Stage 1b: Determining willingness to sell social media data
Now, participants have the opportunity to sell up to three data sets from their personal Facebook, Instagram, TikTok accounts (if existent). The order of networks is randomized in the experiment. Stage 1 is divided into three different treatments. Treatments are randomly assigned before the experiment starts and, thus, do not depend on participants’ answers.
If a participant has none of the aforementioned social network accounts, they skip stage 1 and proceed directly to stage 2.
By varying the (number of) possible buyers, the treatments test whether the willingness to sell data depends on the recipients’ type: a research institution (Research), an external private firm (One-Ext) or different external private firms for each sequential selling decision (Diff-Ext). In the external buyer treatments (One-Ext and Diff-Ext) we forward the data to the external company.
All three treatments use the BDM mechanism: Participants can choose a price between EUR 0 and EUR 30 for each data set they are willing to sell. We are aware that telling participants about the price range of the BDM-mechanism is not without loss of generality (Bohm et al, 1997). Nonetheless, previous work (e.g. Benndorf and Normann, 2018, Schubert et al. 2021) implies EUR 0 - 30 being an appropriate range for the elicitation mechanism in this case.
Then, the computer draws a random number between 0 and 30. If the random number is greater than or equal to the chosen price and if the lottery – described in the next paragraph – draws the person, the data will be sold and the seller receives the value of the random draw as a payout. Otherwise, no sale will occur.
After a participant has played all consecutive BDM-rounds his earnings enter a lottery, at the end of the experiment the lottery draw takes place and selects one out of ten participants to sell their data.
To illustrate, assume that a participant chooses a price of EUR 0. She will sell her data with a probability of 100 %. If, for example, the random draw is 10, the participant receives a payout of EUR 10 if she is selected by the lottery in the end.
Participants always have the option to explicitly opt out of the sale by clicking on the “Do not sell data” button. Correspondingly, they have to agree to the sale of their data of each network separately by ticking a checkbox and clicking on the button “Sell data at least for the selected price”. The combination of checkbox and button is a double opt-in structure that ensures that the participants fully understand the consequences of their decision.
In the Research treatment we inform participants that their data will be sold to us as Researchers. The One-Ext and Diff-Ext treatments focus on the external validity of the experiment. Here, participants not only sell their data to us, but explicitly agree that we may pass on their data sets to one external firm (One-Ext). The Diff-Ext treatment differs from One-Ext in that now each individual data set is passed on to a different external firm.
The three external private firms that cooperate with us and take participants data are all online retailers. Participants obtain access to additional information about the firms. The firms receive the data from us free of charge and comply with data protection regulations.
For the experiment, we focus on evaluating the behavior of participants in the experiment, i.e., what prices they choose for their data, how sequential selling decision influence their behavior and how the willingness to sell differs between treatments.

Stage 2: Questionnaires on personality traits, social network usage, privacy concerns, and demographic characteristics
To better explain the behavior of participants in stage 1, the second stage includes questionnaires on personality traits, social media usage, privacy concerns, risk preferences and demographic characteristics. For the questionnaire on personality traits, we employ the Big Five Inventory (German short version, BFI-K) scale (Rammstedt and John, 2005). The questions on social network usage behavior include, among others, the frequency of use, the number of friends and the age of the respective accounts. Further, we measure privacy concerns using the standardized German version (Pape, 2018) of the Concerns for Information Privacy (CFIP) scale by Smith et al. (1996), ask participants a general risk question (Dohmen et al., 2005) and collect demographic characteristics to control for age, income, or gender effects.
By intention, participants complete all questionnaires that might affect their valuation of personal data after the BDM mechanism. As evident from the literature, the elicited valuation of personal data can be easily influenced by providing additional information beforehand (e.g. Winegar and Sunstein (2019) or Collis et al. (2021)).
If we observe high dropout rates due to participants' exhaustion in stage 2 of our trial run, we might have to adjust the order of the experiment.
Randomization Method
randomization done by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
up to 400 online participants
Sample size (or number of clusters) by treatment arms
We aim for at least 100 Research Treatment, 100 One-Ext Treatment, 100 Diff-Ext Treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
The “Rat für Ethik in der Forschung (Ethikrat) der Universität Siegen” [Council for Ethics in Research (Ethics Council) of the University of Siegen]
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials