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The demand effect in WTP elicitation

Last registered on February 15, 2021
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Pre-Trial

Trial Information

General Information

Title
The demand effect in WTP elicitation
RCT ID
AEARCTR-0007210
Initial registration date
February 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
February 15, 2021, 11:41 AM EST

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

Locations

Country
China
Region

Primary Investigator

Name
Yu Gao
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-02-18
End date
2021-02-28
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Abstract
Experimenter demand effect is a concern in all non-natural experiments with human participants, which refers to changes in behavior by participants trying to infer the experimenter’s objective (Zizzo 2010). For example, participants who believe the researcher wants to promote energy-efficient appliance adoption might show higher willingness-to-pay (WTP) than they otherwise would. In this study, we apply the method proposed by De Quidt et al. (2018) to measure the upper and lower bound of experimenter demand effect of an information treatment intervention. By exposing participants to demand treatments, which are likely to be more informative than implicit signals about demand in typical information intervention studies, we measure the strongest possible treatment effect under th
External Link(s)

Registration Citation

Citation
Gao, Yu. 2021. "The demand effect in WTP elicitation." AEA RCT Registry. February 15. https://doi.org/10.1257/rct.7210-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
See uploaded file.
Intervention Start Date
2021-02-18
Intervention End Date
2021-02-28

Primary Outcomes

Primary Outcomes (end points)
Willingness-to-pay
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will follow the method proposed by De Quidt et al. (2018) to measure the upper and lower bound of experimenter demand effect of an information treatment intervention.
Experimental Design Details
Randomization Method
randomization will be done by the survey platform.
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
220 per group.
Sample size: planned number of observations
220 per group * 9 groups
Sample size (or number of clusters) by treatment arms
220 per group * 9 groups
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
power.t.test(n = NULL, delta = 4, sd = 21, power = 0.8, type = "one.sample", alternative ="two.sided")
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
GSM
IRB Approval Date
2021-01-08
IRB Approval Number
2021-04
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