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Changing the System, Not the Seeker

Last registered on May 18, 2021

Pre-Trial

Trial Information

General Information

Title
Changing the System, Not the Seeker
RCT ID
AEARCTR-0007685
Initial registration date
May 18, 2021
Last updated
May 18, 2021, 9:41 AM EDT

Locations

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Primary Investigator

Affiliation
Boston University

Other Primary Investigator(s)

PI Affiliation
University of Oregon

Additional Trial Information

Status
In development
Start date
2021-05-18
End date
2023-03-31
Secondary IDs
Abstract
We will assess the effect of multiple organization-level treatments on the propensity of investors to invest in a startup. We will assess this variable in multiple ways including evaluation on a scale, and more qualitative evaluation.
External Link(s)

Registration Citation

Citation
Lall, Saurabh and Amisha Miller. 2021. "Changing the System, Not the Seeker." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.7685-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Researchers have designed three interventions which an investment organization making investments in early-stage startups will apply to treatment investors as they evaluate startups.
1) Prompting consistent inquiry
2) Evaluating demonstrated competence
3) Sharing prior evaluations
Intervention Start Date
2021-05-25
Intervention End Date
2022-09-30

Primary Outcomes

Primary Outcomes (end points)
Dependent Variable: The dependent variable is the propensity to invest in a startup .
Primary Outcomes (explanation)
Dependent Variable: The dependent variable is Yijp – the propensity to invest in a startup by i – the investor, j – the startup, and p – the paired program.
We will measure the dependent variable using two methods. We will build our model around the scales and the qualitative proportion measurements. Method 1 and 2 are what we used to build our model:
Binary: Before meeting each company, each professional investor will be asked “Do you want to meet this company?” [Definitely want to meet, Would meet if there’s time, No thank you]. The first two options will be treated as binary responses. First, 1 = definitely want to meet and 0 = other. Second 1 = meet / other, 0 = No thank you.
Qualitative: Each professional investor will be asked what additional information they need from the investor. We will assess the proportion of promotion vs. prevention-focused questions (Kanze et al. 2018).
Performance-reward bias: Normalized scale (Male normalized qualitative proportion – Female normalized qualitative proportion) – see Castilla (2008). Intuitively, if you get the same scale rank, what is the difference between the qualitative score by the gender of the entrepreneur?

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will assess the effect of multiple treatments on the propensity of investors to invest in a startup. We will assess this variable in multiple ways including evaluation on a scale, and more qualitative evaluation.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
individual.

Planned Number of Clusters
Each trainee investor is a cluster and will make at least 9 decisions.
Each professional investor is a cluster and will make at least 3 decisions.
Planned Number of Observations
At least 1,500 individual investor decisions, from at least 200 individual investors.
Sample size (or number of clusters) by treatment arms *
For treatment 1 and treatment 2, I will have a sample size of at least 1,500 individual investor decisions, and 456 on female founders. These stem from at least 200 individual investors.
Power calculation: Minimum Detectable Effect Size for Main Outcomes
The minimum detectable effect size for the ANCOVA calculations is 0.225.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
200 investors
Sample size: planned number of observations
1,500
Sample size (or number of clusters) by treatment arms
100 investors treatment, 100 investors control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect size for the ANCOVA calculations is 0.225.
IRB

Institutional Review Boards (IRBs)

IRB Name
Boston University IRB
IRB Approval Date
2020-08-21
IRB Approval Number
5690X
Analysis Plan

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