Response of angel investors to information on emission reduction potential of start-ups

Last registered on December 12, 2024

Pre-Trial

Trial Information

General Information

Title
Response of angel investors to information on emission reduction potential of start-ups
RCT ID
AEARCTR-0014943
Initial registration date
December 08, 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
December 12, 2024, 11:38 AM EST

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

Locations

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

Request Information

Primary Investigator

Affiliation
London Business School

Other Primary Investigator(s)

PI Affiliation
London Business School
PI Affiliation
London Business School
PI Affiliation
IESE Business School

Additional Trial Information

Status
In development
Start date
2024-12-13
End date
2025-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project we will study whether the emission reduction potential (ERP) of climatech start-ups affects investors' behaviour and financial decisions. Recently, reliable and fine grained ERP measures having been created by third parties that allow to have an estimation of the amount of carbon emissions that a particular start-up may be able to reduce (as compared to the state of the art). The climatech sector has received much attention and funds, but no evidence exist to date about whether ERP affects start-ups financial success in funding stages or latter outcomes (e.g., IPO). The project as a whole involves a regression analysis using Pitchbook data (a database tracking startups and investors), and two field experiments. In this pre-registration we are preregistering the second experiment.

The setting of our field experiment is a set of 21 early-stage climatech start-ups that will be evaluated by 25 angel investors (that are part of an angel investor network in South East Asia). The outcomes of interest (our dependent variables) are the evaluation score and the investment interest as expressed by the 25 angel investors. Our randomization of start-ups will be done "within investors", meaning that for each investor we will randomly select start-ups to be in the control or one of two treatment conditions. The randomization will be performed on the standardized template that evaluators will receive that describe the startups. This template as the usual information that early stage investor evaluate, such as description of the founding team, description of the idea, assessment of potential market, etc. One section of this template will have information on ERP, describing what ERP is, and the source of the ERP information we use. We will experimentally manipulate the ERP information in this section. The control group will indicate that the ERP information is "not available for this start-up". The first treatment condition will indicate that "This start-up is below the median ERP across all the start-ups evaluated in this program". The second treatment will indicate that "This start-up is above the median ERP across all the start-ups evaluated in this program". The comparison of the two treatment and the control will indicate whether the presence of ERP information affects investors' evaluation and selection. The comparison between the two treatments will indicate whether the level of reductions in emissions affects investors' evaluation and selection.
External Link(s)

Registration Citation

Citation
Brahm, Francisco et al. 2024. "Response of angel investors to information on emission reduction potential of start-ups." AEA RCT Registry. December 12. https://doi.org/10.1257/rct.14943-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-12-13
Intervention End Date
2025-01-10

Primary Outcomes

Primary Outcomes (end points)
We will have four likert question (1 to 5), with 1 "definitively not", 2 "probably not", 3 "Might or Might not", 4 "Probably yes", 5 "Definitively yes":

Would your organisation invest in this startup?
Would you invest in this startup?
Do you believe that this startup will be a commercial success?
Would you like to connect to this startup?
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The setting of our field experiment is a set of 21 early-stage climatech start-ups that will be evaluated by 25 angel investors (that are part of an angel investor network in South East Asia). The outcomes of interest (our dependent variables) are the evaluation score and the investment interest as expressed by the 25 angel investors. Our randomization of start-ups will be done "within investors", meaning that for each investor we will randomly select start-ups to be in the control or one of two treatment conditions. The randomization will be performed on the standardized template that evaluators will receive that describe the startups. This template as the usual information that early stage investor evaluate, such as description of the founding team, description of the idea, assessment of potential market, etc. One section of this template will have information on Emission Reductions Potential (ERP), describing what ERP is, and the source of the ERP information we use. We will experimentally manipulate the ERP information in this section. The control group will indicate that the ERP information is "not available for this start-up". The first treatment condition will indicate that "This start-up is below the median ERP across all the start-ups evaluated in this program". The second treatment will indicate that "This start-up is above the median ERP across all the start-ups evaluated in this program". The comparison of the two treatment and the control will indicate whether the presence of ERP information affects investors' evaluation and selection. The comparison between the two treatments will indicate whether the level of reductions in emissions affects investors' evaluation and selection.
Experimental Design Details
Not available
Randomization Method
The experiment will be performed via qualtrics. We have coded in qualtrics the randomization of the treatments as follow: for each evaluator, we will randomly select 7 startup to be in the control group, 7 to be in the low ERP treatment, and 7 to be in the high ERP treatment.
Randomization Unit
Within evaluator, the randomization unit is the start-up. But given that across evaluators a start-up will end up in expectation 1/3 in control, 1/3 in low ERP treatment and 1/3 in high ERP treatment, the "real" randomization unit is the startup-evaluator pair. We have 21 x 25 = 525 pairs.

In order to avoid "order effects" in the evaluation, we will randomize the order in which start-ups will be evaluated by evaluators.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We don't randomize at the cluster level. However, evaluators are clusters, thus we will cluster our standard errors at the evaluator level.
Sample size: planned number of observations
We have 21 startups that will be ebaluated by 25 investors, and thus we will have 21 x 25 = 525 observations.
Sample size (or number of clusters) by treatment arms
Control = 175 start-up/evaluator pairs
High ERP Treatment = 175 start-up/evaluator pairs
Low ERP Treatment = 175 start-up/evaluator pairs
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
According to our calculation, we will be able to detect approximately 16% of a standard deviation, assuming 35% reduction in standard deviation from the addition of startup and investor fixed effects. With this assumption, the MDE is around 24%.
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Ethics Committee -- London Business School
IRB Approval Date
2024-11-14
IRB Approval Number
REC972