Learning to Accept Public Taxes on Global Warming and Pollution: A Game of Two Cities

Last registered on October 08, 2024

Pre-Trial

Trial Information

General Information

Title
Learning to Accept Public Taxes on Global Warming and Pollution: A Game of Two Cities
RCT ID
AEARCTR-0013408
Initial registration date
September 11, 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
September 17, 2024, 11:39 AM EDT

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

Last updated
October 08, 2024, 6:45 AM EDT

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

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Primary Investigator

Affiliation
Univ Rennes & CREM CNRS, France

Other Primary Investigator(s)

PI Affiliation
Université Gustave Eiffel
PI Affiliation
Univ Rennes & CREM

Additional Trial Information

Status
In development
Start date
2024-06-24
End date
2024-10-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The study examines the acceptability of environmental taxes (local air pollutant tax and carbon tax) in the field of urban transportation. It employs a laboratory economic experiment in which participants make commute mode choices (public transport vs. passenger vehicle) and determine whether or not they agree to implement environmental taxes on passenger vehicle mobility. It is hypothesized that, by experiencing the benefits of the environmental taxes, participants will find them more acceptable. However, the question of a substitution effect regarding the acceptability of each possible tax remains open.
External Link(s)

Registration Citation

Citation
Denant-Boemont, Laurent, Frédéric Martinez and Tanguy Richard. 2024. "Learning to Accept Public Taxes on Global Warming and Pollution: A Game of Two Cities ." AEA RCT Registry. October 08. https://doi.org/10.1257/rct.13408-2.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
The objective of this economic laboratory experiment is to assess the acceptability of environmental taxes on commuter mobility. To this end, we construct an experimental design in which residents of two regions are asked to vote in favor of environmental taxes that, by increasing the cost of mobility in private vehicles, encourage people to reduce their use of these vehicles in favor of non-polluting public transportation. To increase tax acceptance, we implement a number of treatments consisting of implementing different taxes before the vote. The distinctive feature of this research is its focus on identifying potential trade-offs between a ’local tax’ on air pollution and a ’global tax’ on greenhouse gas emissions.
We will test five main hypothesis:
H0 : a. The acceptance of the tax on local pollution will increase after the congestion tax implementation.
b. The acceptance of the tax on global pollution will increase after the congestion tax implementation.
H1 : a. The acceptance of the tax on local pollution will increase after its implementation.
b. The acceptance of the tax on global pollution will increase after its implementation.
H2 : a. Acceptance of the tax on local pollution decrease after the implementation of the tax on global pollution.
b. Acceptance of the tax on global pollution decrease after the implementation of the tax on local pollution.
H3 : a. Acceptance of the tax on local pollution after a given treatment is not dependent on the order of the two treatments : implementation of local pollution and congestion taxes and implementation of global pollution and congestion taxes.
b. Acceptance of the tax on global pollution after a given treatment is not dependent on the order of the two treatments : implementation of local pollution and congestion taxes and implementation of global pollution and congestion taxes.
H4 : a. The private vehicle usage is lower when all three taxes are implemented, than when none are.
b. It is lower with the local pollution tax and the congestion tax than with only the congestion tax.
c. It is lower with the global pollution tax and the congestion tax than with only the congestion tax.
d. It is lower with the congestion tax than with no tax.
Intervention Start Date
2024-09-11
Intervention End Date
2024-10-09

Primary Outcomes

Primary Outcomes (end points)
The experiment has two primary outcomes:
• The proportion of participants who chose private vehicles for their commute travel.
• Participants’ bids for the implementation of taxes.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
- Inequity Aversion at the individual level,
- CRT: Cognitive Reflection Test,
- Psychosocial constructs evaluation (consideration for future consequences, like Environmental Self-Identity – See Collet et al 2022 –, New Ecological Paradigm Survey ...),
- Socio-demographic variables
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
20 sessions are organized, each with 20 participants. The participants are divided into 2 groups of 10. The groups of 10 participate together in a game where they are again divided into 2 cities of 5 people. In each round of the game, the participants are asked to choose between using a private car or public transport. The two options have different fixed costs. The choice of private vehicle induces congestion costs, which increase with the number of other private vehicle choices in the same city. The number of private vehicles affects all residents of a city through a local pollution cost and all residents of both cities through a global pollution cost. The Pigouvian tax used to reach the optimum
has been calculated beforehand and is divided into three fractions depending on the congestion externality, the fraction depending on the local pollution externality, and the fraction depending on the global pollution externality. The game is played in several stages:
1. Trials:
• 2 rounds without taxes
• Votes on whether to implement a local pollution tax / global pollution tax
2. Paid experiment:
• 5 rounds without taxes
• Votes on whether to implement a local pollution tax / global pollution tax
• 5 rounds with only the congestion tax
• Votes on whether to implement a local pollution tax / global pollution tax
• 5 rounds with the congestion tax and the local pollution tax
• Votes on whether to implement a local pollution tax / global pollution tax
• 5 rounds with the congestion tax and the global pollution tax
• Votes on whether to implement a local pollution tax / global pollution tax
• 5 rounds with all three taxes
• Votes on whether to implement a local pollution tax / global pollution tax
• 5 rounds with the congestion tax and, the local or global pollution tax (depending on a randomly selected votes outcomes).
In all cases, the amount of the tax is redistributed equally among the participants. We implement a simulator on Otree allowing participants to play with taxes and the number of car users to see the effect on their costs. To test for a possible order effect, half of the groups (200 participants) are run with an inversion of the local and global tax levels. Assignment to one or the other
order is random.
Voting system:
The voting system employs a bid mechanism analogous to that described by Messer et al. (2010):
1. Each (N) participant is endowed with a voting endowment (E), and is required to offer an amount (bi) to support a policy (taxes).
2. A random number R is generated between bmin and bmax. If the median bid is higher than R, then all participants pay R (won E-R), and the tax is adopted.
If the random number is lower than the median bid, the tax is rejected, and all participants receive their inital endowment E.
Payments:
The remuneration is divided down into:
1. The remaining part of the voting endowment.
2. One of the 4 stages of the game is randomly drawn to serve as the basis for compensation, by subtracting the cumulative costs of congestion, local pollution, and global pollution from an initial endowment. The various taxes levied are deducted if the participant chooses to use a private vehicle, and then redistributed equally among all participants.
3. Inequity aversion and CRT are remunerated.
4. All participants earn a flat participation fee.
More information can be found in the analysis plan.
Experimental Design Details
Not available
Randomization Method
The order assignment is randomized through computer using Python.
Randomization Unit
The variation of the order between local and global taxes are randomized at the group level (10 participants).
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
A power analysis was conducted based on previous work by Janush et al. (2020) and Hartman (2012). The analysis revealed that 40 groups of 10 participants would be required. However, the voting system employed in this study differs from that used in Janush et al. (2020). Consequently, a power analysis will be conducted on the trial data (20 respondents) to provide a more comprehensive analysis and
potentially enhance the statistical power.

Further details can be found in the analysis plan.

Janusch, N., Kroll, S., Goemans, C. et al. Learning to accept welfare-enhancing policies: an experimental investigation of congestion pricing. Experimental Economics 24, 59–86 (2021). https://doi.org/10.1007/s10683-020-09650-2 Ajouter au projet Citavi par DOI

Hartman, J.L. Special Issue on Transport Infrastructure: A Route Choice Experiment with an Efficient Toll. Networks and Spatial Economics 12, 205–222 (2012). https://doi.org/10.1007/s11067-009-9111-1
Sample size: planned number of observations
We will have a total of 400 participants. For each of the 5 within treatment, we will have 5 mobility choices and one vote bid per participant.
Sample size (or number of clusters) by treatment arms
20 groups follow the initial order, and 20 follow the inverted order.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Analysis plan

MD5: eb1889cbcda7ba5f6f2c48fba2e89252

SHA1: d9e3d18617fb9047a7f53b6e8e127007935f7885

Uploaded At: October 08, 2024