The effects of upfront payments as a de-risking tool on follow-up investments

Last registered on June 24, 2024

Pre-Trial

Trial Information

General Information

Title
The effects of upfront payments as a de-risking tool on follow-up investments
RCT ID
AEARCTR-0013811
Initial registration date
June 17, 2024

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
June 24, 2024, 2:04 PM EDT

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

Locations

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Primary Investigator

Affiliation
City College of New York

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
In development
Start date
2024-07-01
End date
2024-08-08
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates whether upfront payments provided to impact enterprises in Social Impact Incentives (SIINC) transactions serve as a de-risking tool for investors, thereby increasing their likelihood of investment. Using a deductive approach, the research design incorporates stratification of investors based on key criteria: Type of Investor, Investment Thesis, and Average Investment Ticket Size. Two balanced cohort samples, Cohort 1 (control) and Cohort 2 (treatment), are randomly assigned to receive either the standard SIINC without upfront payments (Start-up A) or a SIINC with a 20% upfront payment (Start-up B). The hypothesis posits that the inclusion of upfront payments in SIINC transactions will enhance investors' propensity to conduct due diligence and pursue investment opportunities by 25%, effectively serving as a de-risking mechanism.

Investors are asked whether they would engage in due diligence for potential investment and to rate the influence of the SIINC contract on their decision. Stratified investor segments include commercial venture capitalists, and impact investors, considering their varying degrees of impact orientation and risk perception.

The findings are anticipated to reveal a significant increase in investment likelihood for Start-up B, especially among impact-oriented investors and those with investment theses aligned with health tech. This study aims to provide insights into how upfront payments in SIINC transactions can strategically reduce investment risk, thereby facilitating greater investment in early-stage impact enterprises. The results are expected to have significant implications for the design of SIINC transactions and the broader field of impact investing.
External Link(s)

Registration Citation

Citation
Chen, Wendy, Ouafaa Hmaddi and Saurabh Lall. 2024. "The effects of upfront payments as a de-risking tool on follow-up investments." AEA RCT Registry. June 24. https://doi.org/10.1257/rct.13811-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-07-03
Intervention End Date
2024-07-31

Primary Outcomes

Primary Outcomes (end points)
Likelihood to initiate due diligence
Whether investors need to learn more information about the startup
Likelihood to ask for a follow up meeting
Likelihood to recommend the opportunity to other investors
Likelihood to ask to see the business plan
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Potential mechanisms: fit with investment thesis; SIINC upfront investment; fit within ticket size; revenue model and market attraction
Other potential mechanisms might be deducted from baseline data
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Randomly (stratified) assign one of two startups based on the SIINC investment they received to investors.
Experimental Design Details
Not available
Randomization Method
Stata randomization. Log file is uploaded
Randomization Unit
investor
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 120
Sample size: planned number of observations
About 120
Sample size (or number of clusters) by treatment arms
120
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Texas Tech University Human Research Protection Program
IRB Approval Date
2024-03-08
IRB Approval Number
IRB2022-171 I