Revealing Reference Dependence through Budgetary Decisions

Last registered on February 25, 2025

Pre-Trial

Trial Information

General Information

Title
Revealing Reference Dependence through Budgetary Decisions
RCT ID
AEARCTR-0015383
Initial registration date
February 14, 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
February 25, 2025, 10:28 AM EST

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

Locations

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Primary Investigator

Affiliation
National University of Singapore

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-02-19
End date
2025-12-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study aims to understand reference dependence for decision making under risk using budget choice design. I examine choice consistency without assuming any changing reference point, and compared that to choice consistency assuming certain kinds of reference points. My hypothesis is that changing reference points play a substantive role in decision making, but the specific reference point that best explains an individual's decision making is heterogeneous.
External Link(s)

Registration Citation

Citation
Wang, Ao. 2025. "Revealing Reference Dependence through Budgetary Decisions." AEA RCT Registry. February 25. https://doi.org/10.1257/rct.15383-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-02-19
Intervention End Date
2025-12-15

Primary Outcomes

Primary Outcomes (end points)
CCEI index
Primary Outcomes (explanation)
The CCEI index will be computed using the GRID method (Polisson, Quah, & Renou, 2020). This method can be flexibly applied to various reference points under different specification assumptions (e.g., concavity, convexity, bounded derivatives, rank dependence), provided that the utility function is locally non-satiated.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design follows a within-subject structure. The decision problem resembles that of Choi, Fisman, Gale, and Kariv (2007), where subjects allocate money between two accounts, termed Account X and Account Y, according to a budget line constraint. Subjects will make 48 decisions of this form, and one randomly selected decision will determine their final earnings. For this randomly selected decision, a die will be rolled to determine which account balance determines payment at the end of the experiment. Final earnings consist of a baseline payment of 15 SGD, plus the earnings from the selected budgetary choice.

The primary innovation in this design lies in the mapping between subjects’ choices and the resulting lottery. Each decision problem features a Fixed Payment F, which remains unaffected by choices on the budget line. The payment mechanism is as follows:
- If the die lands on 1 or 2, the subject receives the balance in Account X.
- If the die lands on 3, 4, or 5, the subject receives Fixed Payment F.
- If the die lands on 6, the subject receives the balance in Account Y.

Subjects will complete 48 decision problems, divided into three parts.

- Within each part, the Fixed Payment F remains constant but changes across parts.
- The possible values of F are 3, 11, and 33, with their order randomized. The primary consideration for these values is ensuring sufficient variation in fixed payments.
- Each part contains 16 budget line problems, where subjects allocate money between Account X and Account Y.
- The budget lines remain the same across parts, but the order in which they appear is randomized for each part and subject.

The trade-offs between Account X and Account Y are defined by the following budget constraints. The primary consideration here is to ensure sufficient variation and intersection across different budget lines, so that the CCEI index (for models of interest) remains sufficiently low in a randomly generated dataset (Bronars test).
#1 Y = 30 - 1.5X
#2 Y = 36 - 2X
#3 Y = 38.4 - 2.2X
#4 Y = 42 - 2.5X
#5 Y = 45.6 - 2.8X
#6 Y = 48 - 3X
#7 Y = 50.4 - 3.2X
#8 Y = 54 - 3.5X
#9 Y = 31.5 - 0.625X
#10 Y = 33 - 0.75X
#11 Y = 34.5 - 0.875X
#12 Y = 36 - X
#13 Y = 42 - 1.5X
#14 Y = 48 - 2X
#15 Y = 50.4 - 2.2X
#16 Y = 54 - 2.5X


Below is a list of candidate reference points. Each reference point is a numerical value unless explicitly defined in the xOy coordinate system or specified according to existing theoretical models (in which case, the authors’ names are provided).
[I] Choice-Dependent Reference Point
1. Payment in X Account
2. Payment in Y Account
[II] Budget Set-Dependent Reference Points (these points are defined in the xOy coordinate system)
3. (In xOy coordinates) The allocation where the X Account balance is equal to the Y Account balance
4. (In xOy coordinates) The allocation where (probability of X × X Account balance) = (probability of Y × Y Account balance)
5. (In xOy coordinates) The midpoint of the budget line
6. (In xOy coordinates) The point where the horizontal coordinate is the maximal balance achievable in Account X, and the vertical coordinate is the maximal balance achievable in Account Y.
[III] Choice-Irrelevant Reference Point
7. Fixed Payment F
[IV] Hybrid of Choice-Dependent and Choice-Irrelevant Reference Points
8. Kőszegi-Rabin CPE
9. Bell-Loomes-Sugden Disappointment Aversion, or the expected value of the chosen lottery
10. Minimum Payment
11. Median Payment
12. Maximum Payment
[V] Hybrid of Budget-Dependent and Choice-Irrelevant Reference Point
13. (In xOy coordinates) Allocation where the X Account balance is equal to the Fixed Payment balance F
14. (In xOy coordinates) Allocation where the Y Account balance is equal to the Fixed Payment balance F
15. (In xOy coordinates) A budget-set reference point (3, 4, 5, 6, 7, 13, 14) if at least one of these reference points dominates the Fixed Payment F; otherwise, the reference point is set to zero.
16. (In xOy coordinates) A budget-set reference point (3, 4, 5, 6, 7, 13, 14) if at least one of these reference points is dominated by the Fixed Payment F; otherwise, the reference point is set to zero.
17. Minimax Criterion
18. Maximin Criterion
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer.
Randomization Unit
Randomization at individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
300
Sample size: planned number of observations
300
Sample size (or number of clusters) by treatment arms
300
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
NUS Department of Economics Ethics Review Committee
IRB Approval Date
2024-12-05
IRB Approval Number
N/A