The Demand for Non-linear Payoffs: Experimental Evidence

Last registered on November 09, 2020

Pre-Trial

Trial Information

General Information

Title
The Demand for Non-linear Payoffs: Experimental Evidence
RCT ID
AEARCTR-0006721
Initial registration date
November 08, 2020
Last updated
November 09, 2020, 10:42 AM EST

Locations

Region

Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
Harvard Business School
PI Affiliation
University of Toronto

Additional Trial Information

Status
In development
Start date
2020-01-01
End date
2021-12-31
Secondary IDs
Abstract
Does security design affect household investment decisions? If so, which payoff design can nudge households into making the right savings or investment decisions? By contrast, can banks use the design of financial products to extract rents from investors by exploiting a gap between how household perceive payoffs and the fair value of these payoffs is? We analyze these research questions by implementing a series of investment experiments. We aim to measure demand elasticity to payoff designs and their parameters, as well as estimate their impact on investor behavior and surplus.

External Link(s)

Registration Citation

Citation
Celerier, Claire , Yueran Ma and Boris Vallee. 2020. "The Demand for Non-linear Payoffs: Experimental Evidence." AEA RCT Registry. November 09. https://doi.org/10.1257/rct.6721-1.0
Experimental Details

Interventions

Intervention(s)
We randomly assign participants into conditions with different investment options, and investigate the investment allocation decisions.
Intervention Start Date
2020-04-01
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
Investment allocations into different investment options in each condition.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomly assign participants into conditions with different investment options (different security designs). We will also measure preference parameters, to study why different individuals make different investment decisions.
Experimental Design Details
Randomization Method
Randomization done by computer algorithm
Randomization Unit
We directly randomize individual participants into different conditions.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
Around 5000 individuals
Sample size (or number of clusters) by treatment arms
100 to 200 individuals per condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Toronto
IRB Approval Date
2018-01-29
IRB Approval Number
35640
IRB Name
Harvard University
IRB Approval Date
2019-08-21
IRB Approval Number
IRB19-1117
IRB Name
University of Chicago
IRB Approval Date
2019-08-01
IRB Approval Number
IRB19-0989

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