Estimating Preferences from Strictly Concave Budget Restrictions

Last registered on February 05, 2023
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Pre-Trial

Trial Information

General Information

Title
Estimating Preferences from Strictly Concave Budget Restrictions
RCT ID
AEARCTR-0009837
Initial registration date
November 21, 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
December 05, 2022, 2:49 PM EST

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

Last updated
February 05, 2023, 3:13 PM EST

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

Locations

Country
Germany
Region
North Rhine-Westphalia

Primary Investigator

Name
Holger Gerhardt
Affiliation
University of Bonn
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Rafael Suchy
PI Affiliation
University of Oxford
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2022-11-22
End date
2022-12-07
Keywords
Behavior, Lab
Additional Keywords
(strictly) convex budgets, (strictly) concave budget restrictions, risk preferences, time preferences, structural estimation
JEL code(s)
C91, D01, D81, D90
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We propose an experimental method, Strictly Concave Budget Restrictions, to determine preference parameters precisely with the lowest possible number of observed choices. Our approach extends a method that is commonplace in economics for estimating preference parameters: the analysis of choices that were made subject to linear budget restrictions. Compared to other elicitation methods, budget-based approaches allow for relatively precise estimation of the preference parameters that govern individuals’ choices. This is the case, however, only as long as individuals choose interior allocations. It has turned out across numerous experimental studies that the majority of choices from linear budgets are corner allocations. The main idea of Strictly Concave Budget Restrictions is to encourage interior choices by making corner allocations extremely unattractive. Just like with linear budgets, the estimation of preference parameters is based on rates of substitution, such that simple and transparent estimation strategies are available, and the method is applicable to various domains (e.g., risk, time, and social preferences). In a series of experiments, we investigate whether our method indeed drastically reduces the frequency of corner allocations and whether it improves, compared to linear budget restrictions, the ability to estimate parameters on the individual level and to characterize interindividual heterogeneity.
External Link(s)

Registration Citation

Citation
Gerhardt, Holger and Rafael Suchy. 2023. "Estimating Preferences from Strictly Concave Budget Restrictions." AEA RCT Registry. February 05. https://doi.org/10.1257/rct.9837-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We investigate a novel method: estimating preference parameters from decisions under strictly concave budget restrictions. This method extends an existing method: estimating preference parameters from decisions under linear budget restrictions.

We compare the performance of the novel method to the performance of the existing method. To do so, the participants of our study make intertemporal decisions and decisions under risk under both strictly concave budget restrictions and linear budget restrictions. The order of the type of budget restrictions that our participants face is randomized between participants.
Intervention Start Date
2022-11-22
Intervention End Date
2022-12-07

Primary Outcomes

Primary Outcomes (end points)
The combination of payments that a participant chooses from the budget restriction offered in the respective round.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We propose an experimental method, Strictly Concave Budget Restrictions, for the estimation of preference parameters, in particular parameters governing choices under risk and intertemporal choices.

Our approach extends a method that is commonplace in economics for estimating preference parameters: the analysis of choices that are made subject to linear budget restrictions. Compared to other elicitation methods, budget-based approaches allow for relatively precise estimation of the preference parameters that govern individuals’ choices. This is the case, however, only as long as individuals choose interior allocations. It has turned out across numerous experimental studies that the majority of choices from linear budgets are corner allocations. Our central idea to overcome this issue is to make the budget restriction a strictly concave function, thereby making corner allocations unattractive.

We conduct an online study consisting of two experiments. In the first experiment, we estimate time preferences, and in the second experiment, we estimate risk preferences, using both linear and strictly concave budget restrictions. We will investigate whether our method reduces the frequency of corner allocations, compared to linear budget restrictions, and whether it improves the ability to estimate preference parameters on the individual level, thereby also improving the characterization of interindividual heterogeneity.
Experimental Design Details
Randomization Method
Within-subject design: The order in which the two types of budget restrictions are presented to the participants is randomized by the computer. So is the order of the various budget restrictions of each type.
Randomization Unit
Individual level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
150 participants.
Sample size: planned number of observations
200 participants: 100 participants in the study on the elicitation of time preferences and 100 participants in the study on the elicitation of risk preferences. Of the 100 participants in the time preference study, 50 will also participate in the risk preference study.
Sample size (or number of clusters) by treatment arms
All treatments are within-subject.

In the time preference study, each participant will make 82 decisions of which one half involve linear budget restrictions, while the other half involves strictly concave budget restrictions.

In the risk preference study, each participant will make 46 decisions of which one half involve linear budget restrictions, while the other half involves strictly concave budget restrictions.

The number of decisions and the number of participants was chosen such that we can compare our results to those of Choi et al. (2007, https://doi.org/10.1257/aer.97.5.1921)—who collected 50 decision each from 93 subjects—and to those of Andreoni and Sprenger (2012, https://doi.org/10.1257/aer.102.7.3333)—who collected 45 decisions each from 97 subjects.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Oxford Economics Department’s Research Ethics Committee (DREC)
IRB Approval Date
2022-08-10
IRB Approval Number
ECONCIA21-22-47
Analysis Plan

Analysis Plan Documents

Preregistration for “Estimating Preferences from Strictly Concave Budget Restrictions”

MD5: fc3613817c780b8e00caadfa24b2fc87

SHA1: 0df07aeaf4c64075b5d9f3ff0f9ba765954b20a4

Uploaded At: November 21, 2022

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
November 30, 2022, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
November 30, 2022, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
100 participants in the time preference study on 2022-11-22 and 100 participants in the risk preference study on 2022-11-30. Among the latter, 50 participants had participated in the time preference study, while the other 50 participants had not.
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
100 × 82 = 8,200 allocation decisions in the time preference study. 100 × 46 = 4,600 allocation decisions in the risk preference study
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials