Probabilistic Weather Forecasts and Agricultural Decision Making in Rural India

Last registered on September 05, 2023
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Pre-Trial

Trial Information

General Information

Title
Probabilistic Weather Forecasts and Agricultural Decision Making in Rural India
RCT ID
AEARCTR-0011526
Initial registration date
June 09, 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
June 15, 2023, 4:43 PM EDT

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

Last updated
September 05, 2023, 12:23 AM EDT

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

Locations

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Primary Investigator

Name
Vaishnavi Surendra
Affiliation
Precision Development
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Other Primary Investigator(s)

PI Name
Shawn Cole
PI Affiliation
Harvard University
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PI Name
Tomoko Harigaya
PI Affiliation
Precision Development
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Additional Trial Information

Status
On going
Start date
2023-08-05
End date
2024-12-31
Keywords
Agriculture, Behavior, Environment & Energy, Lab
Additional Keywords
Risk, Forecasts, Climate Change
JEL code(s)
C91, D81, O12, O13, Q54
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Weather uncertainty is a significant source of agricultural production risks, and is increasingly salient in the context of climate change. Short- to medium-range weather forecasts could help farmers optimize the timing of agricultural practices, increasing returns to agricultural investment. However, smallholder farmers in most parts of the developing world do not have access to highly localized, accurate forecasts, nor are forecasts customized to convey weather conditions relevant for agricultural decision-making. In this study, we investigate whether accurate, relevant-for-context, probabilistic, medium-range weather forecasts can aid farmer decision-making through a lab-in-the-field experiment. We will analyze whether climate change salience, probability training, prior luck impact choices in the experiment and willingness-to-pay for weather forecasts.
External Link(s)

Registration Citation

Citation
Cole, Shawn, Tomoko Harigaya and Vaishnavi Surendra. 2023. "Probabilistic Weather Forecasts and Agricultural Decision Making in Rural India." AEA RCT Registry. September 05. https://doi.org/10.1257/rct.11526-3.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Farmers are randomly assigned to one of the following groups: (1) climate change salience information treatment; (2) probability training and climate change salience information treatments; (3) control.

1. The climate change salience information treatment involves farmers watching a video describing changing weather patterns globally and locally, and the impact it has on coffee farming.
2. The probability training information treatment involves farmers watching a probability training video.
3. In round 1 of the experiment, farmers will receive a few hypothetical scenarios that involve choosing a location with specific weather conditions out of two locations, for which they see weather forecasts. Once a location is chosen, farmers will indicate their confidence in their choice ('points-at-stake'), and then in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather.
4. In round 2 of the experiment, farmers will receive a few hypothetical cultivation practice-related scenarios that involve choosing an action following a weather forecast. Once an action is taken, in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather.
5. Finally, we will elicit farmers’ willingness-to-pay for accurate, customized, probabilistic weather forecasts.
Intervention Start Date
2023-08-05
Intervention End Date
2023-12-31

Primary Outcomes

Primary Outcomes (end points)
Willingness to pay for probabilistic medium-range weather forecasts; choosing the correct forecast; choosing the action with the higher expected payoff.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will recruit small- and medium-holder coffee farmers in Karnataka, India. Farmers will be randomly assigned to (1) climate change salience information treatment, (2) probability training and climate change salience information treatment, (3) control.

Each farmer will answer survey questions, watch their assigned video(s), and play a hypothetical decision-making game where decisions depend on weather realizations and decisions are made after receiving a weather forecast. The game consists of two rounds. In the first round, farmers will receive hypothetical scenarios in which they choose between two locations with different weather forecasts, picking the location where a weather event is more (less) likely to occur. After their choice, a weather realization will occur in-game. Farmer payoffs depend on realized weather. In the second round, farmers will receive hypothetical agricultural scenarios, and choose whether to take an action or not, following a weather forecast. After their choice of action, a weather realization will occur in-game. Farmer payoffs depend on realized weather.

After the end of the game, we will elicit farmers’ willingness-to-pay for accurate, medium-range probabilistic weather forecasts.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted using a random number generator in Stata, and within the survey software.
Randomization Unit
Individual-level (stratified), and within individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
At least 1200 farmers (actual number depends on number of participants enrolled).
Sample size: planned number of observations
At least 1200 individuals (actual number depends on number of participants enrolled).
Sample size (or number of clusters) by treatment arms
504 farmers receiving the climate change salience information treatment; 348 farmers receiving the climate change salience and probability training information treatment; 348 farmers in the control group (actual numbers depends on number of participants enrolled).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University
IRB Approval Date
2023-05-30
IRB Approval Number
IRB23-0302
IRB Name
Health Media Lab, Inc
IRB Approval Date
2023-03-09
IRB Approval Number
2243
IRB Name
Institute for Financial Management and Research
IRB Approval Date
2023-04-19
IRB Approval Number
N/A
Analysis Plan

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