Lottery incentives and carbon pricing rebates

Last registered on January 13, 2025

Pre-Trial

Trial Information

General Information

Title
Lottery incentives and carbon pricing rebates
RCT ID
AEARCTR-0015002
Initial registration date
January 11, 2025

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
January 13, 2025, 1:59 PM EST

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

Locations

Region

Primary Investigator

Affiliation
Agricultural University of Athens

Other Primary Investigator(s)

PI Affiliation
Texas A&M University

Additional Trial Information

Status
In development
Start date
2025-01-10
End date
2025-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Carbon pricing implementation is gaining attention in the literature as an instrument to reduce carbon emissions to meet environmental targets (Narassimhan et al. 2018; Byce 2018). There are several market systems that have been proposed with carbon pricing or using a market-based approach to assign a price on carbon dioxide (CO2) emissions as a way to reduce greenhouse emissions. The implementation of such a system depends on whether there is sufficient public support for it. A key policy-relevant question for carbon pricing is whether using a carbon pricing rebate would increase public support. One of the potential drawbacks of such a policy is that it is expensive to implement as it requires to return a sizable portion of the amount of carbon price tax collected.

Recent literature in behavioral and experimental economics proposes that consumers' behavior towards a lottery is similar to a certain outcome if the level of the lottery is reasonably attainable. More specifically, Ahles, Palma and Drichoutis (2024) find that the incentives produced by a 10% lottery, produce the same outcomes in an incentivized real-market than a 100% realization. In this study, we implement lottery rebates to observe if the consumer reaction from these lottery incentives produces similar benefits than using the full rebate scheme. This is very useful as a policy instrument as implementing a rebate with 10% likelihood of implementation results in a significant reduction of the implementation cost of such a policy.

References:
Ahles, Amelia, Marco A. Palma, and Andreas C. Drichoutis. "Testing the effectiveness of lottery incentives in online experiments." American Journal of Agricultural Economics (2024).

Boyce, James K. "Carbon pricing: effectiveness and equity." Ecological Economics 150 (2018): 52-61.

Narassimhan, Easwaran, Kelly S. Gallagher, Stefan Koester, and Julio Rivera Alejo. "Carbon pricing in practice: A review of existing emissions trading systems." Climate Policy 18, no. 8 (2018): 967-991.
External Link(s)

Registration Citation

Citation
Drichoutis, Andreas and Marco Palma. 2025. "Lottery incentives and carbon pricing rebates." AEA RCT Registry. January 13. https://doi.org/10.1257/rct.15002-1.0
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Experimental Details

Interventions

Intervention(s)
The study is composed of two parts.

*Part 1*: Subjects will participate in an open-ended incentivized choice experiment (Corrigan et al. 2009) akin to a multiple price list task where they’ll have to indicate how many units of a fictitious good they’d like to purchase. Subjects will go through a list of 10 prices and for each price they will indicate how many units of the good they want given that the first unit gives them a value of 4, the second a value of 3 and the third a value of 2. The payoff associated with a price level and units is the sum of the values of the units purchased minus the price of the units. The prices are progressively increasing in a way that a person should choose to buy 3 units for low prices, 2 units for medium prices, 1 unit for higher prices and no units for very high prices. Subjects will be told that researchers have committed upon completion of the study on buying pollution rights via Compensators (https://www.compensators.org/) a charitable non-profit organization with the purpose of reducing surplus carbon emission allowances, and permanently remove them from the market. By doing so industries are forced to reduce their CO₂ emissions and the scarcity of emission allowances leads to price increases, which in turn makes low-emission technologies more competitive. However, each unit they purchase reduces researcher’s contribution by 100 lbs, so if a person chooses to buy all 3 units we will not buy any pollution rights. If a person chooses to purchase 2 units of the good the researchers will purchase 100 lbs of pollution rights, if they purchase 1 unit of the good the researchers will purchase 200 lbs of pollution rights and if they do not purchase any unit, the researchers will purchase all 300 lbs of pollution rights. Subjects will be told that one decision will be randomly selected for payment at the end of the experiment from Part 1.

*Part 2*: Subjects will be asked to select whether on top to any earnings from Part 1, they would like to have earnings from an additional decision added to their payoff. More specifically, subjects will be asked whether they want to be paid for (a) one randomly determined decision for low price levels (incentives are to purchase 3 units of the item for these price levels) or (b) if they’d prefer to be paid for one randomly determined decision for medium and high price levels (incentives are to purchase <3 units for these price levels).

- CONTROL treatment: Subjects go through Part 1 and Part 2 in sequence as described above.
- INTERVENTION treatment: Like the CONTROL treatment, but the following rule is applied to Part 2: If subjects select they prefer to be paid for one randomly determined decision for medium and high price levels, they receive $1 on top to all other earnings (cash back).
- HYPOTHETICAL treatment: Subjects are told that any earnings from Parts 1 and 2 are hypothetical and do not count.
- 100% treatment: Subjects are told that any earnings from Parts 1 and 2 will be paid with certainty.
- 10% treatment: Subjects are told that any earnings from Parts 1 and 2 will be paid with 10% chance.

Corrigan, Jay R., Dinah Pura T. Depositario, Rodolfo M. Nayga Jr., Ximing Wu, and Tiffany P. Laude. "Comparing Open-Ended Choice Experiments and Experimental Auctions: An Application to Golden Rice." American Journal of Agricultural Economics 91, no. 3 (2009): 837–853.
Intervention Start Date
2025-01-10
Intervention End Date
2025-02-28

Primary Outcomes

Primary Outcomes (end points)
- Binary variable of whether subjects choose option (b) in Part 2 vs. option (a). Option (b) includes price levels that maximize payoff for a lower number of units than option (a).
- Number of units purchased in Part 1.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study has a 2x3 design resulting in 6 cells:
1. CONTROL + HYPOTHETICAL
2. CONTROL + 100%
3. CONTROL + 10%
4. INTERVENTION + HYPOTHETICAL
5. INTERVENTION + 100%
6. INTERVENTION + 10%
Experimental Design Details
Not available
Randomization Method
All randomizations are performed by the software program (Qualtrics)
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
no clusters
Sample size: planned number of observations
870 subjects
Sample size (or number of clusters) by treatment arms
at least 145 subjects/treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We define the power 1 − β to be the probability of rejecting the null hypothesis at the two-sided α level of significance. Τhe null hypothesis is that the outcome probabilities in the two groups are equal and the alternative hypothesis is that they take the unequal anticipated probabilities p1 and p2. If the trial has equal sample sizes n in each group, then a popular formula for the total sample size required is (Julious and Campbell, 2012; Marley-Zagar et al., 2023). n =(z_{1-α/2}*SQRT(2*pa*(1-pa))+z_{1-b}*SQRT(p1*(1-p1)+p2*(1-p2)))^2 / ((p2-p1)^2) where z_{1-α/2}=1.96 for α=5%, z_{1-β}=0.84 for β=80% and pa= (p1+p2)/2 Woerner et al. (2024) find a 62.9% vote for carbon pricing support across all conditions and we take this value as p1 = 0.629. Given a minimum detectable difference of 15% between the two groups (p2 = 0.779), we need at least 145 subjects per treatment. Julious, Steven A., and Michael J. Campbell. "Tutorial in Biostatistics: Sample Sizes for Parallel Group Clinical Trials with Binary Data." Tutorial in Biostatistics 31, no. 24 (2012): 2904–2936. Special Issue in Honor of Jerome Cornfield on the Centennial of His Birth. Marley-Zagar, Ella, Ian R. White, Patrick Royston, Friederike M.-S. Barthel, Mahesh K. B. Parmar, and Abdel G. Babiker. "Artbin: Extended Sample Size for Randomized Trials with Binary Outcomes." The Stata Journal 23, no. 1 (March 2023): 24–52. Woerner, Andrej, Taisuke Imai, Davide D. Pace, and Klaus M. Schmidt. "How to Increase Public Support for Carbon Pricing with Revenue Recycling." Nature Sustainability 7 (2024): 1633–1641.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Texas A&M
IRB Approval Date
2025-01-08
IRB Approval Number
STUDY2024-1534