Onset of Offsets: The Role of Social Signaling in Mitigating Climate Change

Last registered on January 07, 2025

Pre-Trial

Trial Information

General Information

Title
Onset of Offsets: The Role of Social Signaling in Mitigating Climate Change
RCT ID
AEARCTR-0012632
Initial registration date
November 30, 2023

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 06, 2023, 8:25 AM EST

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

Last updated
January 07, 2025, 11:33 AM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-01-13
End date
2025-01-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Despite being a top concern for global welfare, policymakers have struggled to implement taxes to address climate change. This has led to an increasing reliance on voluntary actions taken by firms and consumers to combat carbon emissions. In this paper, I experimentally test a non-standard policy that publicizes voluntary consumer carbon mitigation, leveraging social rewards to increase uptake. Specifically, I show that posting names of carbon offset purchasers online is an effective tool to encourage voluntary carbon mitigation, as confirmed by experimentally estimated demand curves. Further, I show that social rewards vary heavily by perceived market penetration of offsets. Uptake increases vastly among those with the lowest perceptions of carbon offset market penetration but is only slightly impacted among those with moderate perceptions. I then estimate a structural model of demand for prosocial actions in the face of social rewards to understand the implications of my findings on optimal subsidy policy. To avoid crowding out social incentives, I show that optimal subsidies for consumer carbon mitigation technologies should start out small at low participation rates and ramp up as these technologies become more common.
External Link(s)

Registration Citation

Citation
Pallottini, Ashton. 2025. "Onset of Offsets: The Role of Social Signaling in Mitigating Climate Change." AEA RCT Registry. January 07. https://doi.org/10.1257/rct.12632-2.1
Experimental Details

Interventions

Intervention(s)
There are two elements of randomization. The first is whether or not decisions to buy the carbon offsets are public. In the public group, those who buy carbon offsets will be allowed to provide their name to be posted online on a website of our design. This website is promoted on an Instagram page with thousands of followers, as participants are informed. The group who is not public will not have any of these options, they will just decide whether to purchase or not under status quo conditions.

The additional layer of randomization involves altering consumer beliefs about the market penetration of carbon offsets. To do this, each participant is sent a signal which varies perceptions about market penetrations before any decisions about buying offsets are made. This creates exogenous variation in beliefs which we leverage to determine how social rewards vary by these beliefs of market penetration.
Intervention (Hidden)
There are two elements of randomization. The first is whether or not decisions to buy the carbon offsets are public. In the public group, those who buy carbon offsets will be allowed to provide their name to be posted online on a website of our design. The website is available at https://www.onset-of-offsets.com/. This website is promoted on an Instagram page with thousands of followers, as participants are informed. The Instagram page is available at https://www.instagram.com/onsetofoffsets/. The group who is not public will not have any of these options, they will just decide whether to purchase or not under status quo conditions.

The additional layer of randomization involves altering consumer beliefs about the market penetration of carbon offsets. To do this, each participant is sent a signal which varies perceptions about market penetrations before any decisions about buying offsets are made. This creates exogenous variation in beliefs which we leverage to determine how social rewards vary by these beliefs of market penetration.

Belief elicitation and alteration require a bit more discussion. To elicit beliefs, we first ask participants to guess how many people taking the survey self-reported having purchased a carbon offset before. This is incentivized for correctness. Since this measures how common each person thinks carbon offsets are, we refer to them henceforth as "first order beliefs." After this, we then ask people to guess what the average first order belief was, again incentivized for correctness. As these measure beliefs about beliefs, we refer to them as "second order beliefs." Second order beliefs are the beliefs which may impact social rewards, and are thus the beliefs which are of most pertinence to our study. We experimentally manipulate these beliefs by sending random signals to consumers and asking them what their posterior second order belief is after seeing the signal. These signals read "We previously surveyed 1,000 United States adults. In this previous survey, we found an average guess of [PROP] out of 1,000 people buying a carbon offset." Here, "[PROP]" randomly varies to be 14, 56, 128, 217, 315, or 438. These values come from averaging guesses in previous pilot studies we ran.
Intervention Start Date
2025-01-13
Intervention End Date
2025-01-31

Primary Outcomes

Primary Outcomes (end points)
Uptake of a carbon mitigation action (carbon offset purchases)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a survey experiment. Participants are recruited online and provide us with demographic information. We then inform them about carbon offsets, in case they are unfamiliar. From here, first- and second-order beliefs on market share of carbon offsets are elicited in an incentivized manner. We provide participants with random signals that shift second-order beliefs and serve as an instrument. Finally, we give participants the choice between receiving a bonus payment or buying an offset, where some participants are told their action will yield their name being posted online while others are not. From here, we ask several questions to determine which mechanisms drive the impact of name-posting as well as to determine endline beliefs about carbon offsets and the survey more specifically.
Experimental Design Details
This is a survey experiment. Participants are recruited online and provide us with demographic information. We then inform them about carbon offsets, in case they are unfamiliar.

From here, first- and second-order beliefs on market share of carbon offsets are elicited in an incentivized manner. To elicit beliefs, we first ask participants to guess how many people taking the survey self-reported having purchased a carbon offset before. This is incentivized for correctness. Since this measures how common each person thinks carbon offsets are, we refer to them henceforth as "first order beliefs." After this, we then ask people to guess what the average first order belief was, again incentivized for correctness. As these measure beliefs about beliefs, we refer to them as "second order beliefs." Second order beliefs are the beliefs which may impact social rewards, and are thus the beliefs which are of most pertinence to our study. We experimentally manipulate these beliefs by sending random signals to consumers and asking them what their posterior second order belief is after seeing the signal. These signals read "We previously surveyed 1,000 United States adults. In this previous survey, we found an average guess of [PROP] out of 1,000 people buying a carbon offset." Here, "[PROP]" randomly varies to be 14, 56, 128, 217, 315, or 438. These values come from averaging guesses in previous pilot studies we ran.

Finally, we give participants the choice between receiving a bonus payment or buying an offset, where some participants are told their action will yield their name being posted online while others are not. Each participant is given $1.50 for participation in the survey but told that they can offset about 145lbs. of carbon using Climate Vault's offsets (market price = $1.50) if they choose to do so. In order to do so, they must agree to forgo a randomly varying amount of their bonus payment. This amount varies to be $0.10, $0.70, or $1.40, allowing us variation in price to estimate demand. The participants are additionally randomized to be in a public group or private group. In the public group, those who buy carbon offsets will be allowed to provide their name to be posted online on a website of our design. The website is available at https://www.onset-of-offsets.com/. This website is promoted on an Instagram page with thousands of followers, as participants are informed. The Instagram page is available at https://www.instagram.com/onsetofoffsets/. The group who is not public will not have any of these options, they will just decide whether to purchase or not under status quo conditions.

From here, we ask several questions to determine which mechanisms drive the impact of name-posting as well as to determine endline beliefs about carbon offsets and the survey more specifically. We ask participants whether public name-posting is influenced by virtue signaling, privacy concerns, being seen as eco-friendly, or potentially promoting offset purchasing to others. The order of these four questions is randomized to ensure that no biases come that favor those questions which are presented first. After this, we ask participants whether they think their name will actually be seen by people. Finally, we conclude by asking participants whether they believe carbon offsets are effective and whether our survey was biased in any way.
Randomization Method
Randomization occurs in the survey, so it is done via Qualtrics' algorithms.
Randomization Unit
Individual level randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3,500 individuals
Sample size: planned number of observations
3,500 individuals
Sample size (or number of clusters) by treatment arms
Treatments are public/private, price variation ($1.40, $0.70, $0.10), and signal for second order beliefs (14, 56, 128, 217, 315, or 438). This yields 36 total treatment groups. The 3,500 individuals will be roughly evenly divided across these 36 groups (~97 each). In practice though, the treatment on second order beliefs is to yield an instrumental variable rather than to make discrete across-group comparisons. Neglecting that variation, there are then 6 treatment groups, for 583 people per group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
The University of Chicago Social and Behavioral Sciences Institutional Review Board
IRB Approval Date
2024-08-13
IRB Approval Number
IRB23-1827

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials