Economic Value of Data: Quantification Using Online Experiments

Last registered on June 15, 2021


Trial Information

General Information

Economic Value of Data: Quantification Using Online Experiments
Initial registration date
June 15, 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
June 15, 2021, 2:29 PM EDT

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



Primary Investigator

Carnegie Mellon University

Other Primary Investigator(s)

PI Affiliation
PI Affiliation
UT Austin
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
We conduct incentive-compatible choice experiments to measure the economic value of digital data. We focus on the value that users place on their personal data related to the biggest social media platform: Facebook. We will use BDM methods to elicit their valuation for their Facebook data. For the baseline valuations, we simply ask them their WTA to part with all of their Facebook data. After eliciting their baseline valuation, we will randomize information to individuals about Facebook settlements after data breaches and as well as the amount of revenue per user generated by Facebook. We will then evaluate how individuals update (or not) their valuations based on the information. Finally, we will randomly draw from a distribution and invite selected individuals to upload their data and receive their stated valuation.
External Link(s)

Registration Citation

Acquisti, Alessandro et al. 2021. "Economic Value of Data: Quantification Using Online Experiments." AEA RCT Registry. June 15.
Sponsors & Partners

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Experimental Details


We will recruit individuals to our survey page and ask them to take the survey on the value of data. We will elicit baseline valuations about their WTA for their Facebook data. After eliciting these baseline valuations, we will randomly assign each individual to one of two information treatment conditions. One condition highlights the fact that Facebook paid each individual $400 in a data breach settlement as part of a lawsuit. Another condition highlights the revenue per user earned by Facebook is about $400 over three years going forward. We allow individuals to revise their valuations if they want to and we measure their valuations again.

These are incentive-compatible since we will draw valuations at the end of the experiment (in real-time) and if their stated valuation is less than the drawn number then the individual will be asked to part with their Facebook data. We use an algorithm to ensure that we never actually access their data but simply record some de-identified summary stats allowing us to ensure that the data is actually legitimate.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcome of interest will be the value individuals assign to their Facebook data. We will ask them for a monetary amount. Additionally, we will also analyze how individuals update their data valuations based on the information treatments.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our survey experiment will start with the baseline valuation question:

(1) What is the minimum amount of money (in US Dollars) you would require to upload all your Facebook data? This includes your posts, photos, messages, likes, and comments.

The information treatments about value accruing to individuals from data breaches and as well as how much money Facebook makes per user will be the following:

(2) To provide some additional context, Facebook recently lost a class action lawsuit for harvesting user data and violating privacy laws and agreed to pay around $400 per user for eligible users (source).

(3) To provide some additional context, Facebook is expected to earn over $400 per North American user over the next three years (source).
Experimental Design Details
Randomization Method
Randomization will be carried by the survey software - Qualtrics.
Randomization Unit
Randomization will be at the individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The number of clusters will be the number of individuals recruited for the survey experiment.
Sample size: planned number of observations
We expect about 5000 individuals in the study.
Sample size (or number of clusters) by treatment arms
We expect a balanced number of individuals across different treatment arms.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Carnegie Mellon University
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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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