Human Behavior and Climate Change: Risk and time preferences under voluntary and institutional mechanisms

Last registered on September 08, 2022

Pre-Trial

Trial Information

General Information

Title
Human Behavior and Climate Change: Risk and time preferences under voluntary and institutional mechanisms
RCT ID
AEARCTR-0010028
Initial registration date
September 07, 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
September 08, 2022, 12:32 PM EDT

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
Centre for Social and Behaviour Change, Ashoka University

Other Primary Investigator(s)

PI Affiliation
University of Leicester
PI Affiliation
Centre for Social and Behaviour Change, Ashoka University

Additional Trial Information

Status
In development
Start date
2022-09-15
End date
2023-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This experiment measures individuals’ present bias and loss aversion parameters and tests their association with individuals’ contribution to a green fund through voluntary and institutional mechanisms (median tax rate).
External Link(s)

Registration Citation

Citation
Dhami, Sanjit, Narges Hajimoladarvish and Pavan Mamidi. 2022. "Human Behavior and Climate Change: Risk and time preferences under voluntary and institutional mechanisms." AEA RCT Registry. September 08. https://doi.org/10.1257/rct.10028-1.0
Experimental Details

Interventions

Intervention(s)
We are interested in investigating how contribution to a green fund, designed to mitigate the risk of climate change, is related to behavioural parameters such as loss aversion and present bias as predicted by our theoretical model.
The experiment has 3 tasks. In task 1 we elicit individual time preferences using CTB method. In task 2 we elicit certainty equivalents of 2 lotteries. This enables us to estimate individual loss aversion parameters. In task 3 we use a strategy method and ask individuals to declare their contribution to the green fund through a voluntary mechanism or institutional mechanism (median income tax). Subjects will be randomly assigned to one of the 4 treatment arms.

Intervention Start Date
2022-10-01
Intervention End Date
2023-02-28

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes are subjects' contributions to a green fund along with subject-specific time and risk preferences.
Primary Outcomes (explanation)
Time preferences are estimated through Convex Time Budgets (CTB) method of Andreoni et al. (2015) in task 1. We elicit certainty equivalents of two lotteries in order to estimate the loss aversion parameter in task 2. We use a bisection procedure with 6 steps to find the value of outcome L such that x>L>0 for which the subject expresses the following indifference:
(-L,0.5;x, 0.5)~0
Since we get an estimate of the parameter of the CRRA utility function in task 1, this indifference enables the estimation of the loss aversion parameter under prospect theory.

Secondary Outcomes

Secondary Outcomes (end points)
Subjects' expectations of the group fund while various factors like a timeline of events and the amount of risky income they receive in the future change. We also collect subject demographics such as age, gender, religion, household income, location and etc.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment has 3 tasks. In task one, we use the CTB method to elicit time preferences. In task 2 we use a bisection procedure to elicit two lotteries' certainty equivalents. Task 3 collects subject contributions toward the green funds.
We use three distinct time periods:t1<t2<t3 and n distinct decision makers.
All decisions are made in the current period at time t1(today). In our baseline treatment,
T1, the endowments for these time periods are (Y,0,Z) where Y>0 and Z>0. At time
t1 decision-makers decide on allocating their current endowment of Y towards
current consumption and green investment. The sum of green investments across
decision-makers in a group determines the stock of green investment, G. At time t3, the endowment of each decision maker, Z, is received with probability p(G) that is increasing in G; this is the good environmental state. However, with probability 1-p(G), a bad environmental state occurs such that each decision maker receives nothing. We collect subject choices for various amounts of Z, t2 and t3 through a strategy method. We have 4 treatment arms. T1 collects voluntary contributions to the green fund from income received today (t1). T2 collects voluntary contributions to the green fund from income received in the future (t2). T3 asks for income tax contributions to the green fund from income received today (t1). And T4 is about income tax contributions to the green fund from income received in future (t2).
Experimental Design Details
Not available
Randomization Method
Randomization is done through random draws by a computer.
Randomization Unit
The unit of randomization is individuals.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We have four treatments.
Sample size: planned number of observations
We aim for 100 subjects in each treatment.
Sample size (or number of clusters) by treatment arms
We are using contribution in public good games to benchmark the standard deviation of subjects' contributions to public goods. Based on Reuben and Riedl (2013) and Andreoni (1988) we opt for the relative pooled standard deviation of 0.10. We assume an effect size of 4 across treatments. With power equal to 80% and alpha of 0.05 for a significant difference in means we require 78 subjects in each treatment. Thus, we are aiming for 100 subjects in each arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ashoka University
IRB Approval Date
2022-07-20
IRB Approval Number
IRB Reference: 39_22_Mamidi
Analysis Plan

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