Forecasts at scale: General equilibrium impacts of climate adaptation

Last registered on November 11, 2024

Pre-Trial

Trial Information

General Information

Title
Forecasts at scale: General equilibrium impacts of climate adaptation
RCT ID
AEARCTR-0013590
Initial registration date
May 10, 2024

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
May 13, 2024, 12:40 PM EDT

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

Last updated
November 11, 2024, 2:57 PM EST

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

Locations

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Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
University of Chicago
PI Affiliation
University of Chicago

Additional Trial Information

Status
In development
Start date
2024-05-19
End date
2026-06-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Climate change will increase weather risk for small-holder farmers in low- income countries. We propose a novel approach to addressing this risk: accurate, long-range forecasts, which provide farmers information well in advance of the growing season about seasonal weather realizations. This project builds on Burlig et al (2024), a successful RCT in 250 villages which demonstrated that forecasts of the timing of the Indian summer monsoon's onset led farmers to substantially adjust up-front investments such as land use, crop choice, and inputs. In this project, we partner with the Indian Ministry of Agriculture and Farmers' Welfare (MoA\&FW) to deliver monsoon onset forecasts to millions of farmers across Telangana, using a low-cost SMS platform operated by the government. We embed a randomized saturation design into this scale-up, enabling us to measure general equilibrium impacts of these forecasts, including information transfer between farmers and impacts on crop prices.
External Link(s)

Registration Citation

Citation
Burlig, Fiona et al. 2024. "Forecasts at scale: General equilibrium impacts of climate adaptation." AEA RCT Registry. November 11. https://doi.org/10.1257/rct.13590-2.0
Experimental Details

Interventions

Intervention(s)
We will send SMS messages containing information about the timing of the onset of the 2024 Indian summer monsoon to farmers in a randomized saturation design, randomized at the mandal (subdistrict) level.
Intervention Start Date
2024-05-19
Intervention End Date
2025-01-01

Primary Outcomes

Primary Outcomes (end points)
monsoon onset beliefs (this version of the PAP)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use a randomized saturation design at the mandal level, randomizing (while stratifying on district) 172 mandals to "high-saturation" (90% of farmers on the SMS extension platform to receive messages) and 171 manuals to "low-saturation" (30% of farmers on the SMS platform to receive messages). We will then randomize message delivery at the individual level according to these saturation levels, stratified by mandal.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Mandal level for saturation, individual level for messages
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
343
Sample size: planned number of observations
Approximately 3,000 individuals in our survey sample
Sample size (or number of clusters) by treatment arms
172 mandals high-saturation, 171 mandals low-saturation, ~800k farmers treatment (no message), ~1.2m farmers control (message).
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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