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Do investors react to information on emissions reduction potential?

Last registered on July 09, 2024

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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

July 09, 2024, 10:02 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

Name

Francisco Brahm

Affiliation

London Business School

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Other Primary Investigator(s)

PI Name

PI Affiliation

London Business School

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PI Name

PI Affiliation

IESE Business School

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Additional Trial Information

Status

In development

Start date

2024-05-20

End date

2024-07-27

Keywords

Behavior, Environment & Energy, Finance & Microfinance

Additional Keywords

Climate tech, Climate change, Emissions

JEL code(s)

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 group will have the following manipulation: i) The header of the email that delivers the newsletter will be "Which climatech technology reduces emissions the most?", ii) The newsletter will have a section devoted to ERP which will be placed at the top of the newsletter, 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 Control group instead will have: i) a different header of the email that will be decided through the usual process that company "X" has for their newsletter, ii) The ERP section will be at the bottom of the newsletter. 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 (==> this is affected by the header of the email), ii) number of clicks on links in the ERP section versus other sections of the newsletter within 10 days of the newsletter release (===> this is affected by "salience of the ERP section", that is, the ERP section located the at the top or at the bottom of the newsletter), and iii) 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. Outcome iii) will only be analyzed if the opening rates of the emails are large (larger 50%) and if the effect of the salience of ERP is considerable.

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. July 09. https://doi.org/10.1257/rct.13616-3.0

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Experimental Details

Interventions

Intervention(s)

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 group will have the following manipulation: i) The header of the email that delivers the newsletter will be "Which climatech technology reduces emissions the most?", ii) The newsletter will have a section devoted to ERP which will be placed at the top of the newsletter, 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 Control group instead will have: i) a different header of the email that will be decided through the usual process that company "X" has for their newsletter, ii) The ERP section will be at the bottom of the newsletter. 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 (==> this is affected by the header of the email), ii) number of clicks on links in the ERP section versus other sections of the newsletter within 10 days of the newsletter release (===> this is affected by "salience of the ERP section", that is, the ERP section located the at the top or at the bottom of the newsletter), and iii) 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. Outcome iii) will only be analysed if the opening rates of the emails are large (larger 50%) and if the effect of the salience of ERP is considerable.

Intervention Start Date

2024-07-17

Intervention End Date

2024-07-27

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 (==> this is affected by the header of the email), ii) number of clicks on links in the ERP section versus other sections of the newsletter within 10 days of the newsletter release (===> this is affected by "salience of the ERP section", that is, the ERP section located the at the top or at the bottom of the newsletter), and iii) 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. Outcome iii) will only be analysed if the opening rates of the emails are large (larger 50%) and if the effect of the salience of ERP is considerable.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Experimental Design

IMPORTANT NOTE: The experiment that was originally pre-registered suffered from a mistake in the execution from the part of company "X". In particular, instead of laying out "emission reduction potential" in the email header, they wrote "ERP" which audience do not know meant "emission reduction potential". This lead to a very small opening rate in the treated group (<7%), in comparison to their usual opening rates (~30%). Given that problem, we decide to eschew that first experiment, do a new one, and thus, register the changes to the experiment. The changes are in the date, the treatments and the outcomes we track (as the first failed run also gave us the opportunity to learn).

We will randomize the treatment (described above) in the newsletter that X will send to its recipients on 17th July 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. Also we will stratify by the randomization that was created for the failed first run of the experiment (control, treatment 1 and treatment 2).

Control and treatment groups:
50% of recipients will be assigned to the control group.
50% of recipients will be assigned to the treatment group.

Treatment: The subject of the email will be "Which climatech technology ". The section about ERP will be at the top of the newsletter (out of 5 sections).
Control: The subject of the email will be "[decided by the company X, but doesn't include climatech". 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 stata software and the list of recipients that X will send a a few days 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).

Supporting Documents and Materials

Institutional Review Boards (IRBs)

IRB Name

IRB Approval Date

IRB Approval Number

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