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.
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 (end points)
Amount invested in the firm (conditional on success probability)
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Success probability communicated to the investor Verification (yes / no)
Secondary Outcomes (explanation)
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
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)