Rationality in decisions under risk and over time

Last registered on July 23, 2015

Pre-Trial

Trial Information

General Information

Title
Rationality in decisions under risk and over time
RCT ID
AEARCTR-0000782
Initial registration date
July 23, 2015

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
July 23, 2015, 8:03 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Ghent University

Other Primary Investigator(s)

PI Affiliation
WZB Berlin Social Science Center
PI Affiliation
University College London

Additional Trial Information

Status
On going
Start date
2015-04-01
End date
2015-09-15
Secondary IDs
Abstract
We investigate decision making processes under risk and over time of a large rural sample in Karnataka, India. We use a budget line task as used in Choi et al. (2007) and Choi et al. (2014), with visual displazs designed to facilitate understanding of the tasks. This allows us to study rationality in decision making under risk in addition of risk preferences per se. We further combine the incentivised experimental tasks with surveys capturing the socio-economic and socio-demographic characteristics of households.
External Link(s)

Registration Citation

Citation
Choi, Syngjoo, Amma Serwaah-Panin and Ferdinand Vieider. 2015. "Rationality in decisions under risk and over time." AEA RCT Registry. July 23. https://doi.org/10.1257/rct.782-1.0
Former Citation
Choi, Syngjoo, Amma Serwaah-Panin and Ferdinand Vieider. 2015. "Rationality in decisions under risk and over time." AEA RCT Registry. July 23. https://www.socialscienceregistry.org/trials/782/history/4829
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We measure individual preferences. As such, we do not have an experimental intervention. Rather, we rely in natural variation on background characteristics. A plausibly exogenous variation is the one in rainfall levels and rainfall onset dates during the monsoon.
Intervention Start Date
2015-04-01
Intervention End Date
2015-09-15

Primary Outcomes

Primary Outcomes (end points)
Risk preferences; rationality in decision making under risk
Time preferences; rationality in decision making over time
Primary Outcomes (explanation)
1) Nonparametric analysis of economic rationality: We first conduct GARP analysis about consistency with utility maximisation. In the risk preferences, our analysis will be the same as in Choi et al. (2007) and Choi et al. (2014). For the time preferences, we can do further analysis about dynamic inconsistencies between two time treatments. In addition to the GARP analysis, we can also conduct a test about stochastic dominace in the risk preference data.

2) Demand analysis
2.1) Nonparametric analysis of demand: we can define a simple statistic of demand behaviour under risk and over time. For the risk preference data, we define the average fraction of money allocated to a cheaper asset as a nonparametric measure of risk tolerance. If the subject is risk neutral, this measure is equal to 1. If the subject is extremely risk averse, this measure is equal to 1/2.

For the time preference data, we can define the average fraction of money allocated to a sooner payment date as a nonparametric measure of impatience. If the subject is very patient, this measure is close to zero. The more impatient the subject is, the higher this measure is.

2.2) Parametric analysis of demand: In this part, we can conduct a model-based estimation of risk preferences and time preferences. For the risk preference data, we intend to use the rank-dependent utility model to estimate both standard risk aversion coefficient and a parameter about probability weighting.

For the time preference data, we can use a variety of models about intertemporal choices. First of all, we can estimate quasi-hyperbolic model with stationary per-period utility function, commonly called (beta, delta) model. We can then extend the analysis by allowing per-period utility function to be time varying.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
RISK: subjects are asked to allocate a budget between two risky assets, each one of which obtains with a probability of 0.5. The assets have different payoffs, so that allocations give an indication of risk preferences (see description in Choi et al. 2007 and Choi et al. 2014).

TIME: subjects are asked to allocate a budget between an earlier and a later time horizon, with differential payoffs according to the time horizon. There is always an upfront delay to avoid differential transaction costs.
Experimental Design Details
Randomization Method
24 villages were randomly chosen out of three Taluks in Ramanagara district. All village inhabitants are eligible to participate. There is no random allocation to treatments at this stage
Randomization Unit
In the randomly chosen villages, all villagers are eligible to participate. In the first round, we target household heads or principal decision makers in the household as participants.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2760
Sample size: planned number of observations
2760
Sample size (or number of clusters) by treatment arms
2760
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

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials