Time and risk preferences and support for climate policy

Last registered on April 13, 2023

View Trial History

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

Country

United States of America

Region

Primary Investigator

Name

Julia Berazneva

Affiliation

Middlebury College

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Jeffrey Carpenter

PI Affiliation

Middlebury College

Contact Investigator

PI Name

Peter H. Matthews

PI Affiliation

Middlebury College

Contact Investigator

PI Name

Delaney Sullivan

PI Affiliation

Middlebury College

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-04-13

End date

2023-06-13

Keywords

Behavior, Environment & Energy

Additional Keywords

information provision, experiment, time preference, risk preference, environmental policy

JEL code(s)

Q51, Q54, D9

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

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

Request Information

Experimental Details

Interventions

Intervention(s)

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

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?

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.

Supporting Documents and Materials

IRB