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Probabilistic Weather Forecasts and Agricultural Decision Making in Rural India

Last registered on June 15, 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.

Locations

Country
India
Region

Primary Investigator

Name
Vaishnavi Surendra
Affiliation
Precision Development
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Shawn Cole
PI Affiliation
Harvard University
Contact Investigator
PI Name
Tomoko Harigaya
PI Affiliation
Precision Development
Contact Investigator

Additional Trial Information

Status
In development
Start date
2023-07-03
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 probability training, climate change awareness, prior luck impact choices in the experiment and willingness-to-pay for weather forecasts. In addition, we will also analyze whether the format in which weather forecasts are communicated impacts choices/decisions.
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. June 15. https://doi.org/10.1257/rct.11526-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Farmers are randomly assigned to four groups: (1) probability training treatment; (2) climate change awareness treatment; (3) probability training and climate change awareness treatments; (4) neither (control).

1. The probability training treatment involves farmers watching an interactive probability training video
2. The climate change awareness treatment involves farmers watching a video describing changing weather patterns globally and locally, the impact it has on coffee farming, and how weather forecasts might help time practices better to mitigate weather effects on production.
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, 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 (Hidden)
Intervention Start Date
2023-07-03
Intervention End Date
2023-12-31

Primary Outcomes

Primary Outcomes (end points)
Willingness-to-pay for accurate, customized weather forecasts; choice of correct forecast; confidence in chosen forecast; choice of action appropriate for realized weather in hypothetical agricultural decisions; game score/payoff.
Primary Outcomes (explanation)
Willingness-to-pay for weather forecasts will be elicited from farmers using the Becker–DeGroot–Marschak (BDM) method.
Choice of correct forecast will be a binary outcome taking a value 1 if the chosen forecast is more (less) likely and 0 otherwise.
Confidence in chosen forecast will be elicited by asking farmers to choose the number of ‘points-at-stake’. So, if the desired weather event occurs, the ‘points-at-stake’ are added to their score, and if the desired weather event does not occur, the ‘points-at-stake’ are deducted from their score.
Choice of action appropriate for realized weather in the hypothetical agricultural decisions will be a binary outcome taking a value 1 if the action is desired in the realized state of the world; and 0 otherwise.
Game score/payoff will be constructed by adding points earned over each decision in the experiment.

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 2 cross-randomized treatments (probability training, climate change awareness).

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
Randomization Method
Randomization is conducted using a random number generator in Stata, and within the survey software.
Randomization Unit
Individual-level, and within individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1200 farmers
Sample size: planned number of observations
18,000 decisions (1200 farmers X 15 decisions) 1200 observations for willingness to pay
Sample size (or number of clusters) by treatment arms
300 farmers in control (placebo video); 300 farmers in treatment 1 (probability training); 300 farmers in treatment 2 (climate change awareness); 300 farmers in treatment 1 + 2 (probability training + climate change awareness)
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

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