Peer effects and financial decision-making I

Last registered on September 17, 2021

Pre-Trial

Trial Information

General Information

Title
Peer effects and financial decision-making I
RCT ID
AEARCTR-0007896
Initial registration date
July 02, 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
July 06, 2021, 10:44 AM EDT

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

Last updated
September 17, 2021, 1:22 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Paderborn University

Other Primary Investigator(s)

PI Affiliation
Paderborn University
PI Affiliation
Leuphana University of Lueneburg

Additional Trial Information

Status
In development
Start date
2021-07-07
End date
2021-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The project investigates the impact of social interactions on individuals' financial decision-making.
External Link(s)

Registration Citation

Citation
Krull, Sebastian, David Loschelder and Matthias Pelster. 2021. "Peer effects and financial decision-making I." AEA RCT Registry. September 17. https://doi.org/10.1257/rct.7896
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Experimental Details

Interventions

Intervention(s)
The project investigates the impact of social interactions on individuals' financial decision-making.
Intervention Start Date
2021-07-07
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
Inspired by the seminal work of Kahneman and Tversky (1979, 1992), we will observe the choices between either a risky lottery or a safe outcome for a list of decision problem. The sum of decisions that individuals have to make allows us to estimate the cash equivalents for different prospects and to quantify investors’ risk preferences.
Primary Outcomes (explanation)
The construction of primary outcome variables follows the original works by Kahneman and Tversky on Prospect Theory and Cumulative Prospect Theory (1979, 1992), respectively. The main variable of interest is the certainty equivalent, which is estimated as the mean value between the lowest accepted value of a certain outcome and its highest rejected value in a given decision problem.

Additionally, we will make use of the ratio of the certainty equivalent to the nonzero outcome of a prospect to fit smoothed curves for participants’ weighting functions (see Prospect Theory; Kahneman & Tversky, 1979).

Following the outline of Tversky & Kahneman (1992), we will first run the following analyses:
- Separately for gains and losses, we will use a nonlinear regression procedure to estimate parameters for the value function and the weighting function, separately for each participant. We will also report cumulative results for all estimated parameters.
- We will study the ratio(c/x) of the certainty equivalent(c) of the prospect to the nonzero outcome(x), as a function of the probability (p) to attain individual and cumulative weighting curves.

We will exclude inconsistent choices for any decision problem as they do not allow to determine the certainty equivalent for the respective prospect. We will also exclude participants who do not show a consistent pattern in their degree of risk aversion.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The project investigates the impact of social interactions on individuals' financial decision-making.
Experimental Design Details
Sampling

We will recruit two samples: one sample consisting of university students, and one sample consisting of professionals. Our student-sample will be recruited from two different universities where students participate in computer-based experiments in the universities’ respective laboratories. The student sample will be split evenly across universities. Both the treatment and control group will contain students from both universities. We split the sample in order to minimize the influence of university-specific student characteristics that may influence the results.
Our professional sample will be collected using the database of a professional sample provider. We purchase access to their database to be able to directly target finance professionals from the field. These professionals will take part in the study via an online platform.
Participants will receive a performance-based financial compensation. The participants’ payoffs will depend on three randomly selected choices and will be adjusted with a predetermined exchange rate to ensure that payoffs equal the hourly wage of a student research assistant, on average.

Task

The participants will face a series of decision problems inspired by Tversky and Kahneman (1992). Student participants will face 56 variants of the following decision problems. Professional participants will face fewer variants due to time constraints. Participants choose between a risky lottery on the one hand (e.g., 25% chance to win 150€ and a 75% chance to win 50€) and a certain outcome on the other hand (e.g., 100% chance to win ##€). For each prospect, seven certain outcomes are shown. These are logarithmically spaced between the lowest and highest outcome of the risky lottery. For each of the certain outcomes, the participant chooses between either the risky lottery or the certain outcome.
In a second step, participants will refine their choices by again deciding between the risky lottery and seven certain outcomes, which now are equally spaced between a value slightly lower than lowest certain outcome accepted in the first set and a value slightly higher than the highest certain outcome rejected.
For each decision problem, internal consistency of choices will be checked after the participants have submitted their choices. If a participant chooses inconsistently within a decision problem, they will be facing the same decision problem once again with the indication that the decisions have been inconsistent. While all participants will be facing the same decision problems, the order will be randomized.

We will further survey all participants for basic demographic information and personality traits:
- gender, age, educational degree, study major, financial expertise,
- extraversion, agreeableness, need for affiliation, social comparison tendencies

In more detail, we will use the following scales:
Evaluation of personality characteristics (on scales of 1-5, 1 being “strongly disagree”, 5 being “strongly agree”):
- extraversion, agreeableness: Big Five Inventory (12 items each Soto & John, 2017)
- Need for affiliation: Steers & Braunstein, 1976 (5 items)
- social comparison: INCOM Scale (Gibbons & Buunk, 1999; 6 items)

Treatment

Participants will be randomly assigned to either a control group or to our treatment.
1) In the control group, participants will face the task as described before without having any interaction with peers. In essence, the control group is a replication of the original work of Tversky and Kahneman (1992).
2) In our treatment, participants will receive information on the payoffs of randomly assigned peers. More precisely, participants will first decide on 50% of the lotteries, assigned at random. For the remaining 50% of the lotteries, participants will learn about the outcome of (partly) randomly matched peers for the respective lottery. That is, for each lottery in the second half (50%) of the lotteries, participants will be presented the payoff that one particular peer received on this lottery. Before participants decide on the first decision problem with peer feedback, they will be assigned and introduced to two peers. For each following lottery they will see the outcome of one of those two peers. In effect, we make use of a stratified sampling technique and ensure that participants observe only payoffs from peers who have shown a consistent pattern throughout their lottery decision problems. Thereby, we de facto create three treatment groups: i) one condition with, on average, more risk averse peers (than the participant before the treatment occurs), ii) one condition with, on average, equally risk averse peers (compared to the participant before the treatment occurs), and iii) one condition with, on average, less risk averse peers (than the participant before the treatment occurs).

Exit survey

We conduct an exit survey using three different pages. The survey contains the following items.
Page 1:
- Do you think the researchers in this study had an agenda? (yes or no)
- If yes, please state what do you think the research agenda was.

Page 2:

What did the authors try to show with this study? (multiple choices allowed)
- The authors tried to show how people make monetary choices.
- The authors tried to show that people evaluate probabilities non-rationally.
- The authors tried to show that people are risk-seeking.
- The authors tried to show that people are influenced by the results of others.
- The authors tried to show that people can make many decisions adequately in a short time

Page 3:

- Please give the names of your two peers (treatment conditions only).
- Do you think your peers were either risk-averse or risk-seeking? (5-point scale; 1 being ”risk-seeking” to 5 being “risk-averse”) (treatment conditions only)
- While playing the lotteries, did you feel closely connected to your peers? (5-point scale; 1 being “strongly disagree” to 5 being “strongly agree”)
- Did a comparison with your peers’ outcomes influence your choices? (5-point scale; 1 being ``strongly disagree'' to 5 being “strongly agree”)
- The comparison with my peers’ outcomes made me take more risky choices than I usually would have? (5-point scale; 1 being “strongly disagree” to 5 being “strongly agree”)
- The comparison with my peers’ outcomes made me take less risk than I usually would have? (5-point scale; 1 being “strongly disagree” to 5 being “strongly agree”)


Anonymity measure: Participants’ study data will be separately stored from their personal information that is needed to handle the performance-based financial compensation.

Miscellaneous: All experiments will be implemented using oTree (Chen et al, 2016).
Randomization Method
Participants will be randomly assigned to a treatment or control condition, with approximately 40 participants going to the control group (for each, the student and the professional sample). Participants in the treatment groups are randomly matched with their peers and therefore, receive a random level of treatment. Randomization will take place automated by oTree (Chen et al, 2016).
Randomization Unit
Randomization will take place on individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
672 participants; 336 student participants and 336 professional participants.
Sample size: planned number of observations
672 participants; 336 student participants and 336 professional participants.
Sample size (or number of clusters) by treatment arms
We intend to sample 336 student participants and 336 professional participants. Given the design of the treatments, we cannot guarantee an equal distribution across treatment groups. Thus, we only assign approx. 40 of participants per sample (student and professional) to the control group; the remainder will be assigned to the treatment group and receive one of our treatments. As the exact treatment that individuals receive depends on their individual risk preferences prior to the treatment, we cannot ensure equal assignment to the three levels of treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We conducted an a-priori sample size analysis using G*Power (Faul, Erdfelder, Buchner, & Lang, 2009), based on the following parameters: α=.05, 1-β=.95, and a moderate population effect size of f=0.25 (d=0.50). Based on this sample size analysis (n=84, λ=15.75, Fcrit=3.03), we intend to recruit a total sample of 336 student participants and 336 professional participants, respectively.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
University Paderborn Ethics Committee
IRB Approval Date
2020-09-07
IRB Approval Number
N/A
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