Do investors react to information on emissions reduction potential?

Last registered on May 30, 2024

Pre-Trial

Trial Information

General Information

Title
Do investors react to information on emissions reduction potential?
RCT ID
AEARCTR-0013616
Initial registration date
May 15, 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
May 21, 2024, 10:50 AM EDT

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

Last updated
May 30, 2024, 2:08 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Affiliation
London Business School

Other Primary Investigator(s)

PI Affiliation
London Business School
PI Affiliation
IESE Business School

Additional Trial Information

Status
In development
Start date
2024-05-20
End date
2024-10-06
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this study we aim to answer the following research question: "“Do investors react to information on the emission reduction potential (ERP) of climate tech start-ups?”. Increasingly there is awareness and methodologies that can estimate the potential in reducing carbon emissions of different technologies and start-ups. Collaborating with a company which, for privacy reasons, we will identify as "X" --- a platform dedicated to provide information on climate tech innovation and that has investors, corporations, researchers, and others as customers --- we will randomize information about ERP in their monthly newsletter. The treatment will be mentioning ERP in the header of the email, and adding a section in the newsletter devoted to ERP which includes a link to a webpage with additional information (which contains general information about ERP, showcases the ERP metrics that X provides in their platform, and provides a downloadable document). The newsletter is sent to investors but also many other subscribers (e.g., corporates, academic, climate curious, etc.), and thus we will stratify the randomization by type of recipient. We will track the following outcomes: i) rate of e-mail opening within 10 days of the newsletter release, ii) number of clicks on links in the newsletter within 10 days of the newsletter release (this captures whether ERP was "additive" in terms of newsletter engagement), iii) for the treated group, number of clicks on links in the ERP section of the newsletter (also, within 10 days of the release of the newsletter) (in combination with ii), this outcome will allow us to explore whether the attention drawn by ERP came at a cost of crowding out attention on the rest of the newsletter --- crowding out by ERP = clicks on links in the control group / clicks on non-ERP links in the treated group), iv) for recipients that are not customers of X, we will study whether there is an increase in the likelihood of becoming a customer of X within 4 months of the release of the newsletter, and v) for recipients that are customers of of X, we will study several daily outcomes within 30 days of the release of the newsletter: times the customer logged into the X platform, times the customer browsed a start-up with ERP information (X provides ERP information for some start-ups), times the customer browsed a start-up without ERP information, and other general engagement metrics that X tracks.
External Link(s)

Registration Citation

Citation
Brahm, Francisco, Xia Li and Claudio Rizzi. 2024. "Do investors react to information on emissions reduction potential? ." AEA RCT Registry. May 30. https://doi.org/10.1257/rct.13616-1.1
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Experimental Details

Interventions

Intervention(s)
In this study we aim to answer the following research question: "“Do investors react to information on the emission reduction potential (ERP) of climate tech start-ups?”. Increasingly there is awareness and methodologies that can estimate the potential in reducing carbon emissions of different technologies and start-ups. Collaborating with a company which, for privacy reasons, we will identify as "X" --- a platform dedicated to provide information on climate tech innovation and that has investors, corporations, researchers, and others as customers --- we will randomize information about ERP in their monthly newsletter. The treatment will be mentioning ERP in the header of the email, and adding a section in the newsletter devoted to ERP which includes a link to a webpage with additional information (which contains general information about ERP, showcases the ERP metrics that X provides in their platform, and provides a downloadable document). The newsletter is sent to investors but also many other subscribers (e.g., corporates, academic, climate curious, etc.), and thus we will stratify the randomization by type of recipient. We will track the following outcomes: i) rate of e-mail opening within 10 days of the newsletter release, ii) number of clicks on links in the newsletter within 10 days of the newsletter release (this captures whether ERP was "additive" in terms of newsletter engagement), iii) for the treated group, number of clicks on links in the ERP section of the newsletter (also, within 10 days of the release of the newsletter) (in combination with ii), this outcome will allow us to explore whether the attention drawn by ERP came at a cost of crowding out attention on the rest of the newsletter --- crowding out by ERP = clicks on links in the control group / clicks on non-ERP links in the treated group), iv) for recipients that are not customers of X, we will study whether there is an increase in the likelihood of becoming a customer of X within 4 months of the release of the newsletter, and v) for recipients that are customers of of X, we will study several daily outcomes within 30 days of the release of the newsletter: times the customer logged into the X platform, times the customer browsed a start-up with ERP information (X provides ERP information for some start-ups), times the customer browsed a start-up without ERP information, and other general engagement metrics that X tracks.
Intervention Start Date
2024-06-06
Intervention End Date
2024-06-13

Primary Outcomes

Primary Outcomes (end points)
We will track the following outcomes: i) rate of e-mail opening within 10 days of the newsletter release, ii) number of clicks on links in the newsletter within 10 days of the newsletter release (this captures whether ERP was "additive" in terms of newsletter engagement), iii) for the treated group, number of clicks on links in the ERP section of the newsletter (also, within 10 days of the release of the newsletter) (in combination with ii), this outcome will allow us to explore whether the attention drawn by ERP came at a cost of crowding out attention on the rest of the newsletter --- crowding out by ERP = clicks on links in the control group / clicks on non-ERP links in the treated group), iv) for recipients that are not customers of X, we will study whether there is an increase in the likelihood of becoming a customer of X within 4 months of the release of the newsletter, and v) for recipients that are customers of of X, we will study several daily outcomes within 30 days of the release of the newsletter: times the customer logged into the X platform, times the customer browsed a start-up with ERP information (X provides ERP information for some start-ups), times the customer browsed a start-up without ERP information, and other general engagement metrics that X tracks.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomize the treatment (described above) in the newsletter that X will send to its recipients on June 2024. The treatment will be stratified by the type of recipients that X identifies (investor, corporate executive/employee, Government/NGO, Start-up founder/operator, Student/academic, Climate curious, other) and by whether the recipients are customers of X or not.
Control and treatment groups:
40% of recipients will be assigned to the control group.
30% of recipients will be assigned to the treatment 1 group.
30% of recipients will be assigned to the treatment 2 group.
Treatment 1: The subject of the email will be "Emission reduction potential, topic2, topic3, and more". The section about ERP will be at the top of the newsletter (out of 5 sections).
Treatment 2: The subject of the email will be "topic2, topic3, Emission reduction potential, and more". The section about ERP will be at 4th in the newsletter (out of 5 sections).

Experimental Design Details
Not available
Randomization Method
Randomization will be performed using a computer and the list of recipients that X will send a week ahead of the date of release of the newsletter.
Randomization Unit
Recipients of the newsletter of X
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
The treatment is not clustered
Sample size: planned number of observations
~ 2,500 newsletter recipients, of which ~580 are investors.
Sample size (or number of clusters) by treatment arms
Considering all newsletter recipients: 1,000 in control, 750 in treatment 1, and 750 in treatment 2. This will be stratified by type of recipient (investor, corporate, academic, etc.) and whether the recipient is also a customer of X.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We can calculate the minimum detectable effect (MDE) for the outcome i) "rate of e-mail opening", for which we have information from previous months of newsletters by X. The average rate of email opening that X experiences is 28%, which means a standard deviation of 44.9%. Our sample size of 2,500 recipients allows us to detect a MDE of 11% of a standard deviation, or approximately 5 percentage points in a simple treated vs control analysis; regarding the investor recipient type, our sample of 580 allows us to detect a MDE of around 25% or 11 percentages points. If we add a baseline and allow for a difference-in-difference analysis, using the opening rate of the newsletter immediately before that of our treatment (May 2024), we can reduce these MDE to 9.5% and 21%, respectively (of course, depending on assumptions on autocorrelation). For the outcomes ii) and iii) regarding the number of clicks, in past newsletter the average number of clicks per recipient is 0.25 (with a standard deviation of 0.43) (the vast majority does 1 or 0 clicks). The sample size of 2,500 allows to detect a MDE of 11% of a standard deviation (equivalent to 0.047 clicks); if performing a dif-in-dif analysis, the MDE is 9.5% of a standard deviation (equivalent to 0.041 clicks). On investors, the sample of 580 allows a MDE of 23% of a standard deviation (equivalent to 0.010 clicks); if performing a dif-in-dif analysis, the MDE is 21.5% of a standard deviation (equivalent to 0.093 clicks).
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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