x

Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Changing the System, Not the Seeker
Last registered on June 14, 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
June 14, 2021 4:20 PM EDT
Location(s)

This section is unavailable to the public. Use the button below to request access to this information.

Request Information
Primary Investigator
Affiliation
Boston University
Other Primary Investigator(s)
PI Affiliation
University of Oregon
PI Affiliation
GIL - World Bank
PI Affiliation
GIL - World Bank
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
Goldstein, Markus et al. 2021. "Changing the System, Not the Seeker." AEA RCT Registry. June 14. https://doi.org/10.1257/rct.7685-1.1.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
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 propensity to invest in a startup.
Primary Outcomes (explanation)
Dependent Variable: The dependent variable is Yp – the propensity to invest in a startup by the paired program. The four paired programs will take place in four geographic regions and include entrepreneurs from across those regions: Sub-Saharan Africa, India, MENA and Latin America. This will result in a total sample of eight programs - four treatment programs and four control programs – with one treatment and one control program in each location. We use fixed effects for the paired program in all regressions. (We will only include fixed effects for the investor in pooled regressions when we join up the sample with the professional investors.)
We will measure the dependent variable using 4 methods.
For the first treatment – prompting consistent enquiry – our primary dependent variable will be qualitative, following Kanze et al. (2018).
For the second treatment – evaluating demonstrated competence – our primary dependent variable will be scales, inspired by Clingingsmith and Shane’s (2018) dependent variable.
1. Scales: Each trainee investor will evaluate each startup on a scale. The baseline evaluation takes place on a 6-point scale. Thereafter, evaluators will use a 24-point scale (control group) and a 32-point scale (treatment group). All scale evaluations are normalized by the program using a z-score.
2. Binary: Each trainee investor will know that the top 2 rated startups will receive investment. Therefore, each trainee investor will carefully consider who they place in the top 2.
3. Qualitative: Each trainee investor will be asked what additional information they need from the startup. Trainee investors will also ask for additional information in conversations, and combined, this will form a secondary dependent variable. All responses will be coded as “promotion-focused” or “prevention-focused”. We will assess the proportion of promotion vs. prevention-focused questions (Kanze et al. 2018).
4. 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 investor

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

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information