Rebate versus matching, again: Does opt-in matter?

Last registered on April 30, 2023

Pre-Trial

Trial Information

General Information

Title
Rebate versus matching, again: Does opt-in matter?
RCT ID
AEARCTR-0010943
Initial registration date
February 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
February 13, 2023, 11:44 AM EST

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

Last updated
April 30, 2023, 6:31 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Osaka University

Other Primary Investigator(s)

PI Affiliation
Kyoto University of Advanced Science
PI Affiliation
Osaka University

Additional Trial Information

Status
On going
Start date
2023-02-10
End date
2025-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Standard economic theory predicts that, when the donation price and all other factors are equal, there should be no difference in individuals’ donation behavior under matching and rebate schemes. For example, a 1:1 matching is equivalent to a 50% rebate. In the former scheme, when one chooses to donate 5000 JPY to a charity, the same amount will be added to this donation, thus making the total amount donated to the charity 10,000 JPY. In the latter scheme, when one chooses to donate 10,000 JPY to a charity, half of the amount will be refunded, making the actual donation expenditure 5000 JPY. Similarly, a 2:1 matching is equivalent to a 33% rebate, and a 4:1 matching is equivalent to a 20% rebate. However, Eckel and Grossman (2003) experimentally reveal that donation rates and average donation expenditures for matching are higher than for rebate. Sasaki, Kurokawa, and Ohtake (2021) use a Japanese nationwide sample and report the findings consistent with Eckel and Grossman (2003).

This study’s purpose is to determine in a randomized controlled trial how treatment effects of matching and rebate change when people can self-select whether to use such schemes or not. Most traditional policy research using randomized controlled trials has measured the causal effects of mandatory policy assignment. However, implementing a policy intervention in a mandatory manner is rare in the real world. This is because mandatory implementation requires a system that enables a policy to be applied to all individuals involved and monitors their adherence to it. Also, a policy must be made mandatory by law, and implementation costs tend to be extremely high. In practice, policies are often applied to only those who choose to accept them, in particular by employing an opt-in scheme, where a policy is not applied by default, but rather only upon request.

Under conditions with self-selection, overall policy impacts will vary depending on the heterogeneous effects across individuals and which individuals self-select to receive the policy. For example, if a policy is widely accepted by people for whom a large (or significant) positive policy effect appears, the overall policy impact will become larger than if the policy intervention was mandated, and thus the policy function more efficiently due to self-selection. Conversely, if those who are likely to experience small or negative effects choose to receive a policy intervention, the overall policy impact will become relatively small, and self-selection will prevent it from functioning efficiently. To accurately understand the real-world implications of policy interventions, it is essential to ascertain the influence of self-selection on policy efficiency. Recent field experimental studies have begun to measure policy intervention effects after considering self-selection, particularly in electricity markets (Wang et al. 2020; Fowlie et al. 2021; Ito et al. 2021; Ida et al. 2022).

This study adds to this emerging literature stream new evidence in the context of charitable giving by measuring the treatment effects of matching and rebate, while considering self-selection.

References:
- Eckel, C.C., & Grossman, P.J. (2003). Rebate versus matching: does how we subsidize charitable contributions matter?. Journal of Public Economics, 87(3-4), 681-701.
- Fowlie, M., Wolfram, C., Baylis, P., Spurlock, C. A., Todd-Blick, A., & Cappers, P. (2021). Default effects and follow-on behaviour: evidence from an electricity pricing program. The Review of Economic Studies, 88(6), 2886–2934.
- Ida, T., Ishihara, T., Ito, K., Kido, D., Kitagawa, T., Sakaguchi, S., & Sasaki, S. (2022). Choosing who chooses: selection-driven targeting in energy rebate programs. National Bureau of Economic Research. (No. w30469).
- Ito, K., Ida, T., & Tanaka, M. (2021). Selection on welfare gains: experimental evidence from electricity plan choice. National Bureau of Economic Research. (No. w28413).
- Sasaki, S., Kurokawa, H., & Ohtake, F. (2022). An experimental comparison of rebate and matching in charitable giving: The case of Japan. The Japanese Economic Review, 73(1), 147-177.
- Wang, W., Ida, T., & Shimada, H. (2020). Default effect versus active decision: evidence from a field experiment in Los Alamos. European Economic Review, 128, 103498.
External Link(s)

Registration Citation

Citation
Sasaki, Shusaku, Takunori Ishihara and Hiroki Kato. 2023. "Rebate versus matching, again: Does opt-in matter?." AEA RCT Registry. April 30. https://doi.org/10.1257/rct.10943-1.1
Experimental Details

Interventions

Intervention(s)
We randomly divide participants into matching treatments, rebate treatments, or a control, and conduct the economic experiment to capture their donation behavior under each assigned condition. We construct two groups, respectively for the 1:1 matching treatments (compulsory and self-selection) and the 50% rebate treatments (compulsory and self-selection).

- 1:1 matching (compulsory): All the participants assigned to this group will select their donation amount under the 1:1 matching, where for every donation amount they pass on to the charity, the experimenter will match it with an additional equal amount.

- 1:1 matching (self-selection): Participants assigned to this group are required to apply in advance if they wish to use the 1:1 matching.

- 50% rebate (compulsory): All the participants assigned to this group will select their donation amount under the 50% rebate, where for every donation amount participants pass on to the charity, the experimenter will refund 50% of the amount to you.

- 50% rebate (self-selection): Participants assigned to this group are required to apply in advance if they wish to use the 50% rebate.
Intervention Start Date
2023-02-17
Intervention End Date
2023-02-24

Primary Outcomes

Primary Outcomes (end points)
- Initially selected amount
- Actual donation expenditure
- Total amount donated to the charity
Primary Outcomes (explanation)
In this economic experiment, participants are informed that, in addition to the basic reward points for answering the survey, one in ten will have a chance to earn another reward, and the additional reward points are worth 1000 JPY. They are then needed to decide how much of the 1000 JPY they are willing to pass on to a social contribution project, assuming they could win and earn this additional reward. If they win and earn 1000 JPY, their donation decision will be carried out as they answer.

Secondary Outcomes

Secondary Outcomes (end points)
- Whether to donate or not
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly divide participants into matching treatments, rebate treatments, or a control, and conduct the economic experiment to capture their donation behavior under each assigned condition. We construct two groups, respectively for the 1:1 matching treatments (compulsory and self-selection) and the 50% rebate treatments (compulsory and self-selection).

- 1:1 matching (compulsory): All the participants assigned to this group will select their donation amount under the 1:1 matching, where for every donation amount they pass on to the charity, the experimenter will match it with an additional equal amount.

- 1:1 matching (self-selection): Participants assigned to this group are required to apply in advance if they wish to use the 1:1 matching.

- 50% rebate (compulsory): All the participants assigned to this group will select their donation amount under the 50% rebate, where for every donation amount participants pass on to the charity, the experimenter will refund 50% of the amount to you.

- 50% rebate (self-selection): Participants assigned to this group are required to apply in advance if they wish to use the 50% rebate.

We explain more details in the attachments of “Theoretical Background” and “Hypothesis Setting and Analysis Plan.”
Experimental Design Details
Not available
Randomization Method
Stratified randomization by a survey company.
The strata are based on age, sex, and residential area.
Randomization Unit
Individuals.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
2,400 individuals
Sample size (or number of clusters) by treatment arms
480 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
According to Sasaki, Kurokawa, and Ohtake (2022), conservative d of the difference between the effects of matching and rebate is 0.25. When we calculate the necessary sample size under the conditions of power=0.8 and alpha=0.05, it becomes 253 for each group. We ensure a sample size of 480 for each group in preparation for the heterogeneity analysis with subgroups. Reference: Sasaki, S., Kurokawa, H., & Ohtake, F. (2022). An experimental comparison of rebate and matching in charitable giving: The case of Japan. The Japanese Economic Review, 73(1), 147-177.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Center for Infectious Disease Education and Research, Osaka University IRB
IRB Approval Date
2023-01-20
IRB Approval Number
2022CRER0120-2
Analysis Plan

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