The People Shadow Price of Carbon

Last registered on June 08, 2022

Pre-Trial

Trial Information

General Information

Title
The People Shadow Price of Carbon
RCT ID
AEARCTR-0009528
Initial registration date
June 01, 2022

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
June 06, 2022, 5:50 AM EDT

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

Last updated
June 08, 2022, 10:39 PM 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
Grenoble Ecole de Management

Other Primary Investigator(s)

PI Affiliation
Grenoble Ecole de Management
PI Affiliation
Grenoble Ecole de Management

Additional Trial Information

Status
In development
Start date
2022-06-02
End date
2023-07-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The goal of this study is to estimate the implicit price of carbon that people apply in their consumption decisions, which we call the People Shadow Price of Carbon (PSPC). We use an artefactual field experiment to recover a distribution of the PSPC among a target population. We also investigate whether extrinsic incentives could crowd-out the PSPC. Our focus is to show heterogeneity in the distribution of the PSPC. Finally, we illustrate the importance of the PSPC by conducting a policy analysis where we vary information and extrinsic incentives in a choice environment where consumers have to choose between bundle of goods and services with different carbon footprints.
External Link(s)

Registration Citation

Citation
Faure, Corinne , Sébastien Houde and Joachim Schleich. 2022. "The People Shadow Price of Carbon." AEA RCT Registry. June 08. https://doi.org/10.1257/rct.9528
Experimental Details

Interventions

Intervention(s)
The study employs the Panel de Recherche du Territoire Grenoblois which is administered by the Grenoble Ecole de Management. We conduct an online survey where we ask participants to choose between holiday packages that differ in their carbon footprint. The main component of the survey consists of a sequence of multiple price list questions and belief elicitations. We vary within and between subject the disclosure of carbon footprint information, which allows us to infer the implicit price of carbon that people apply in this context. We also conduct a discrete choice experiment at the end of the survey to infer participants’ willingness to pay to different components of holiday packages without and with carbon information. Finally, additional demographic and psychographic variables are collected and used to investigate heterogeneity in the People Shadow Price of Carbon.
Intervention Start Date
2022-06-02
Intervention End Date
2022-07-31

Primary Outcomes

Primary Outcomes (end points)
The main dependent variables are outcomes from binary choices used in a Multiple Price List (MPL) procedure. We use the MPL to infer the willingness to pay (WTP) for reducing the carbon footprint of a bundle of goods and services in holiday packages. We also a belief elicitation procedure to infer the beliefs about the difference in carbon footprint between different holiday packages. Using the WTPs from the MPLs and elicited beliefs, we can recover the PSPC for each survey participants. The PSPC is the main quantity of interest, which we use to test various hypotheses and how it varies with respect to key demographic and psychographic variables.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
In the last part of the study, we conduct a discrete choice experiment (DCE) to estimate a demand system for holiday packages in the presence of carbon footprint disclosure. The demand model is estimated using a sequence of 6 binary choices made by each participant.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design consists of a sequence of multiple price lists used to elicit the willingness to pay for holiday packages without and with carbon footprint information. In the baseline multiple price list (baseline MPL), participants are first asked to choose between two holiday packages. Using a sequence of binary choices, the break-even price such that a participant is indifferent between the two packages. Following the baseline MPL, all participants are asked if they consider carbon footprint information in their decision. Beliefs about the difference in the carbon footprints of the two option are elicited. An information treatment that consists of disclosing the carbon footprints of the package is then introduced. Half the participants are treated.

All participants (the treated and non-treated) re-do the same multiple price list procedure to elicit how the willingness to pay for the two packages might have been affected by the information treatment. The second MPL is followed by a belief elicitation to assess whether the information treatment was effective with the treatment group. Using the baseline MPL and the second MPL together with the belief elicitation allows us to infer the People Shadow Price of Carbon.

A scenario with a tax on holiday packages is introduced. For the treatment group, the tax is framed as a carbon tax. A third MPL is used to assess the impact of the tax and its framing. Throughout the three MPLs, the holiday packages are exactly the same. Only the disclosure of carbon-related information varies (within and across participants).

The last module of the survey consists of discrete choice experiment where we vary the components of the holiday packages and their relative prices. Participants have to make a sequence of choices between the different packages. For the treatment group, carbon-footprint information is disclosed.

Additional demographic and psychographic information is collected.
Experimental Design Details
Not available
Randomization Method
The randomization of the information treatment is implemented via Qualtrics. The winners of the lottery is done in office by a computer.
Randomization Unit
Individual (i.e., survey participants)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 500-900 survey participants
Sample size: planned number of observations
About 500-900 survey participants
Sample size (or number of clusters) by treatment arms
Half the sample size
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
5%
IRB

Institutional Review Boards (IRBs)

IRB Name
GEM Research Ethics Committee
IRB Approval Date
2022-06-02
IRB Approval Number
2022 00003
Analysis Plan

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