Self-Verification Mechanisms for Investment Decisions

Last registered on March 01, 2021

Pre-Trial

Trial Information

General Information

Title
Self-Verification Mechanisms for Investment Decisions
RCT ID
AEARCTR-0007061
Initial registration date
January 18, 2021
Last updated
March 01, 2021, 3:44 AM EST

Locations

Region

Primary Investigator

Affiliation
Bard College Berlin

Other Primary Investigator(s)

PI Affiliation
University of Kiel
PI Affiliation
University of Kiel

Additional Trial Information

Status
Completed
Start date
2021-01-19
End date
2021-02-28
Secondary IDs
Abstract
The situation between investors and startups is typically characterized by asymmetric information inducing adverse selection and thus suboptimal investments. To mitigate inefficiencies, investors often engage in costly verification processes referred to as due diligence. Additionally, latest technological innovations help to considerably reduce transaction costs. In this project, we suggest a novel “startup investment game” experiment and a series of treatments to test for the effects of costly verification and self-verification. Depending on the results, a policy conclusion could be to support platforms based on the recent self-verification technologies.
External Link(s)

Registration Citation

Citation
Requate, Till, Aurel Stenzel and Israel Waichman. 2021. "Self-Verification Mechanisms for Investment Decisions." AEA RCT Registry. March 01. https://doi.org/10.1257/rct.7061-1.2000000000000002
Experimental Details

Interventions

Intervention(s)
Our workhorse is an investment game. We implement five treatments with different verification mechanisms: (i) Baseline, (ii) Costly Noisy Verification, (iii) Costly Verification, (iv) Costly Self-Verification, and (v) Self-Verification
Intervention Start Date
2021-01-19
Intervention End Date
2021-02-28

Primary Outcomes

Primary Outcomes (end points)
Amount invested in the firm (conditional on success probability)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Success probability communicated to the investor
Verification (yes / no)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our workhorse is an investment game. We implement five treatments with different verification mechanisms: Baseline, Costly Noisy Verification, Costly Verification, Costly Self-Verification, Self-Verification”

The experiment will be conducted online using Otree (Chen et al., 2016) with participant pool from an economics department experimental lab.
Experimental Design Details
The design is explained in detail in a pdf document attached to this preregistration. The document will become available at the completion of the study.
Randomization Method
All participants are recruited from the same participant pool of an experimental economics lab.
We randomized the experimental sessions using a computer (4-5 sessions per treatment for a total of 20-25 sessions).
In each session, individual rules (investor, firm) are randomly determined by the Otree program.
Randomization Unit
Experimental session
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Between 4 and 5 experimental sessions per treatment for 5 treatments:
A total of of 20-25 experimental sessions.
Sample size: planned number of observations
4 or 5 sessions per treatment for 5 treatments. Each session with about 20 participants: Thus, a total of 400-500 participants, where each pair of participants is an independent observations. Hence, the number of independent observations: 200-250 (i.e., between 40 and 50 per treatment for 5 treatments ).
Sample size (or number of clusters) by treatment arms
Baseline treatment´with 4-5 sessions (80-100 participants);
Costly Noisy Verification treatment with 4-5 sessions (80-100 participants);
Costly Verification treatment with 4-5 sessions (80-100 participants);
Costly Self-Verification treatment with 4-5 sessions (80-100 participants);
Self-Verification treatment with 4-5 sessions (80-100 participants)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

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