Time and risk preferences and support for climate policy

Last registered on April 13, 2023

Pre-Trial

Trial Information

General Information

Title
Time and risk preferences and support for climate policy
RCT ID
AEARCTR-0011263
Initial registration date
April 13, 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
April 13, 2023, 4:48 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Middlebury College

Other Primary Investigator(s)

PI Affiliation
Middlebury College
PI Affiliation
Middlebury College
PI Affiliation
Middlebury College

Additional Trial Information

Status
In development
Start date
2023-04-13
End date
2023-06-13
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
To what extent the support of environmental policies reflects differences in individual time and risk preferences is an outstanding question, which is important for both the design of successful climate policies and to induce behavioral changes required to achieve climate goals. In this study, we design an information provision experiment that activates individual time or risk preferences, by asking individuals to estimate the timing or likelihood of extreme temperatures in their state and for a subset of them providing information about the latest scientific estimates of the timing or likelihood, before asking about their support for climate policies and intended behaviors.
External Link(s)

Registration Citation

Citation
Berazneva, Julia et al. 2023. "Time and risk preferences and support for climate policy." AEA RCT Registry. April 13. https://doi.org/10.1257/rct.11263-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2023-04-13
Intervention End Date
2023-06-13

Primary Outcomes

Primary Outcomes (end points)
We have two primary outcomes. The first is general (“How much would you support a climate policy that is targeted at preparing communities for the impacts of extreme heat?”) and the second is targeted at gasoline consumption (“The current federal tax (per gallon) on gasoline is $0.18, most of which is used for highway maintenance. How much more tax would you be willing to pay if the proceeds were used to lessen climate change and its impacts?”).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We have four secondary outcomes that allow us to examine the external validity of our protocol by comparing our results to previous studies using the same questions. These four questions reference more specific policies (carbon tax, renewable energy mandate, electric vehicle mandate, and drilling/mining on public lands) from the Yale Climate Opinion Maps 2021 (Howe et al. 2015) and are meant to benchmark our findings against the well-known survey of climate policy support.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this study, we design an information provision experiment that activates individual time or risk preferences, by asking individuals to estimate the timing or likelihood of extreme temperatures in their state and for a subset of them providing information about the latest scientific estimates of the timing or likelihood, before asking about their support for climate policies and intended behaviors.
Experimental Design Details
Randomization Method
Our study is between-subjects and the unit of randomization is the participant who is randomized to just one condition: time control, time treatment, risk control, or risk treatment. The study is conducted on Prolific and the data are collected via Qualtrics.
Randomization Unit
Unit of randomization is individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2,800 individuals.
Sample size: planned number of observations
2,800 individuals.
Sample size (or number of clusters) by treatment arms
700 observations per treatment arm: time control, time treatment, risk control, or risk treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations are consistent with the rule of thumb offered in Haaland et al. (2023). Based on the distribution of the main policy support question responses in our pilots, to detect 0.15sd treatment effect (power = 0.8, significance = 0.05) requires between 680 and 699 observations per treatment.
IRB

Institutional Review Boards (IRBs)

IRB Name
Middlebury College Institutional Review Board
IRB Approval Date
2023-03-14
IRB Approval Number
196
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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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