Narrative Willingness to Pay

Last registered on October 31, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
Narrative Willingness to Pay
RCT ID
AEARCTR-0017081
Initial registration date
October 27, 2025

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
October 31, 2025, 8:11 AM EDT

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

Locations

Country
Italy
Region

Primary Investigator

Name
Andrea Albertazzi
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Alessandro Stringhi
PI Affiliation
Prague University of Economic and Business
Contact Investigator
PI Name
Marco Stimolo
PI Affiliation
University of Siena
Contact Investigator
PI Name
Paolo Pin
PI Affiliation
University of Siena
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-11-03
End date
2026-04-09
Keywords
Behavior, Lab
Additional Keywords
Willingness to pay, experiment, narratives, information acquisition, nondiagnostic signal, Bayesian
JEL code(s)
C91; D81; D83; D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study extends another ongoing project pre-registered here: https://doi.org/10.1257/rct.15672-1.0
We build on the framework of the existing project with the aim of exploring how narratives affect the monetary value people attach to nondiagnostic information.
We will replicate a similar Bayesian problem in a controlled experiment, where individuals face uncertainty and use new information to update their beliefs about the true state of the world. New information does not come for free, but signals can be purchased by the participant before stating their belief.

We will randomly assign participants to one of four conditions:
1) C_No_Info: participants engage with an abstract inference problem where acquired signals can only be diagnostic or missing.
2) C_Yes_Info: participants engage with an abstract inference problem where acquired signals can be diagnostic and nondiagnostic.

In the remaining treatments, participants will instead receive a fictional story, framed around identifying a culprit, which mirrors the same uncertainty and signals as the baseline but embeds them in a more emotionally and contextually rich framework.
3) N_No_Info: participants engage with the story, and acquired signals can only be diagnostic or missing.
4) N_Yes_Info: participants engage with the story, and acquired signals can be diagnostic and nondiagnostic.

By comparing differences across treatments, we assess whether the presence of a narrative increases the monetary value participants assign to nondiagnostic information. We expect higher valuations because, in our previous experiment, participants used nondiagnostic information to update their beliefs. Our goal is to show that subjects not only (mis)use nondiagnostic signals but also regard them as informative.
External Link(s)

Registration Citation

Citation
Albertazzi, Andrea et al. 2025. "Narrative Willingness to Pay." AEA RCT Registry. October 31. https://doi.org/10.1257/rct.17081-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We will vary two treatment variables in a 2×2 between-subjects design:
1) Type of context: participants are exposed to either an abstract or a narrative (fictional) story.
2) Signal technology: nondiagnostic signals are either present or absent (i.e., participants see a blank line instead of the nondiagnostic signal).
Intervention Start Date
2025-11-03
Intervention End Date
2026-04-09

Primary Outcomes

Primary Outcomes (end points)
Our primary outcome is the price participants are willing to pay to receive new information, elicited via a Becker–DeGroot–Marschak mechanism (BDM).
Primary Outcomes (explanation)
We elicit willingness to pay (measured in tokens) for the new information. Because the theoretical price depends on the participant’s current belief, we will also compute derived measures—specifically, the absolute and relative deviations from the theoretical price.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In all treatments, participants first read an initial ‘story’. In C_No_Info and C_Yes_Info, the story is presented in an abstract inference frame; in N_No_Info and N_Yes_Info, it is framed as a narrative. All stories, regardless of treatment, involve two mutually exclusive alternatives.

Participants state a prior belief about which alternative is correct.

Next, for each story they play 5 rounds in which they state the price at which they are willing to purchase three new signals. Prices are elicited with an incentivised Becker–DeGroot–Marschak (BDM) mechanism. Depending on treatment, signals can favour one of the two alternatives or be nondiagnostic/missing (i.e., no signal is shown). The ex-ante probabilities of each signal are common knowledge. After each BDM elicitation, participants report a posterior belief.

Each participant completes three different stories (15 rounds in total). Participants are paid for each belief elicitation.
Experimental Design Details
Not available
Randomization Method
At the beginning of each experimental session, the computer will randomly assign each participant to one of the four treatments.
Randomization Unit
The randomization of the treatment is at the individual level. The order of the stories is randomized at the session level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Each experimental session constitutes one cluster for the randomization of the story order. The total number of clusters corresponds to the number of planned sessions, 24.
Sample size: planned number of observations
We plan to run laboratory sessions until we recruit at least 46 participants per experimental condition. Therefore, we will recruit at least 184 participants in total.
Sample size (or number of clusters) by treatment arms
We plan to run laboratory sessions until we recruit at least 46 participants per experimental condition. Therefore, we will recruit at least 184 participants in total.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on theoretical predictions, we estimated the expected effect size. Using a two-sided t-test (beta = 0.8, alpha = 0.05), we determined the stated sample size. We then ran 1000 simulations using a panel regression with the expected effect size and the planned number of observations per treatment. Simulations indicate a power of 0.84 with alpha equal to 0.05. More details can be found in the attached files.
Supporting Documents and Materials

Documents

Document Name
Instructions original language
Document Type
other
Document Description
File
Instructions original language

MD5: aaf5138ff49583f4e49d7c00b491f2e3

SHA1: 1dfd0e54e2cad9e846845985fa824f179163b660

Uploaded At: October 23, 2025

Document Name
Power Simulation
Document Type
other
Document Description
File
Power Simulation

MD5: 63cf71f53137f1e66410ed9bf97c6346

SHA1: 2e2d85168d196a8e9853438ce128f5781037500b

Uploaded At: October 27, 2025

Document Name
Power Calculations
Document Type
other
Document Description
File
Power Calculations

MD5: 3d9ea8e8a702bf676e1cb7c72e9f4a03

SHA1: c4a520791ad9fe339d0917f47036cbb4786914cf

Uploaded At: October 27, 2025

IRB

Institutional Review Boards (IRBs)

IRB Name
Comitato per la RicercA Etica nelle scienze Umane e Sociali – CAREUS
IRB Approval Date
2025-10-07
IRB Approval Number
5/2025
Analysis Plan

Analysis Plan Documents

Analysis plan

MD5: 0713f34833c76b2cdc08a937986d1072

SHA1: a86de6432b9fe62a588752d1453b9d4c003b820d

Uploaded At: October 27, 2025