The Impacts of Cold Storage Infrastructure and Weather Forecasts on Agricultural Outcomes and Trade

Last registered on March 06, 2024
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Pre-Trial

Trial Information

General Information

Title
The Impacts of Cold Storage Infrastructure and Weather Forecasts on Agricultural Outcomes and Trade
RCT ID
AEARCTR-0012900
Initial registration date
February 26, 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
March 06, 2024, 3:30 PM EST

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

Locations

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Primary Investigator

Name
Shweta Bhogale
Affiliation
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Other Primary Investigator(s)

PI Name
Namrata Kala
PI Affiliation
MIT Sloan
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Additional Trial Information

Status
In development
Start date
2024-02-05
End date
2025-05-31
Keywords
Agriculture, Environment & Energy
Additional Keywords
Agricultural Trade, Productivity, Markets, Climate Adaptation
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Absence of cold storage facilities can negatively impact farmers and traders through multiple channels - loss of produce, lower quality of produce, or being forced to sell any perishable produce that remains unsold during hotter times of the day at lower prices to avoid further losses. As temperatures get hotter, cooling solutions for storing produce at agricultural markets could enhance farmer income by increasing the price received and eliminating spoilage losses. Our randomized control evaluation will provide better access to cold-storage facilities to treated traders relative to the control group through a subsidy to use the facility, as well as weather forecasts that allow farmers to better make use of the coldroom access across markets in three districts of Odisha, India. We will measure how access to solar-powered cold-storage infrastructure and weather forecasts influences agricultural trade in terms of price and quantities sold, traders' profits, and any market-level aggregate effects.
External Link(s)

Registration Citation

Citation
Bhogale, Shweta and Namrata Kala. 2024. "The Impacts of Cold Storage Infrastructure and Weather Forecasts on Agricultural Outcomes and Trade." AEA RCT Registry. March 06. https://doi.org/10.1257/rct.12900-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Our study will address constraints of liquidity and apprehension about using new technologies through the first treatment arm that subsidizes access to farmers and traders to encourage the usage of cold storage facilities. This will be bundled with a second treatment arm that provides weather forecasts that allows them to optimize their usage of the coldroom. The control group does not get either but can access the cold storage at full price. This will allow us to test which constraints improve profitability for the traders and whether there are complementarities between the digital service and the subsidy.
Intervention Start Date
2024-02-12
Intervention End Date
2024-09-30

Primary Outcomes

Primary Outcomes (end points)
Our main outcomes of interest are the quantity of produce sold, stored, wasted, and the price received for different crops during the morning and afternoon hours to capture the range of effects from coldroom access. We will also look at longer-term outcomes like farmers' and traders' likelihood of using the coldroom at full price after the study period, time-use, and changes in farmers' cropping decisions as a result of the access to cold storage technology.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experimental sample consists for ~35 markets in Odisha that are getting coldroom infrastructure in April. We will conduct weekly high-frequency surveys in these markets for traders who attend the markets regularly with a particular focus on smallholder farmers and vendors (who aggregate produce across farmers).

Within these markets that are going to receive the coldroom infrastructure, we will randomize access to 3 interventions: a subsidy to store about 100kgs of produce for a month, for 4 months, a bundle of the subsidy with digital solutions that equip farmers with weather forecast information, and weather forecast information only. The control group will not get either, but will be able to use to coldrooms at full price.

To pick up the aggregate effects of coldroom access, we will also conduct the same high-frequency surveys in non-coldroom markets in the same districts, before and after the coldrooms were built. This will allow us to detect spillovers on the control farmers in the experimental markets using an event-study design.

We will also test for three important heterogeneities: (1) differential effects by gender, (2) differential effects depending on whether a trader is a smallholder farmer or a vendor, (3) response to temperature shocks on certain days (for which we will collect hourly ambient temperature information in all markets).

Experimental Design Details
Not available
Randomization Method
Randomization done in the office by a computer
Randomization Unit
Individual level stratified randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
35 experimental markets with 60 sampled farmers in each.

Additional 24 comparison markets.
Sample size: planned number of observations
~3360 traders across experimental and non-experimental markets
Sample size (or number of clusters) by treatment arms
1120 traders in each group across
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
700 traders in each group across 35 experimental markets
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IFMR Human Subjects Committee
IRB Approval Date
2023-12-18
IRB Approval Number
N/A
IRB Name
MIT COUHES
IRB Approval Date
2023-10-19
IRB Approval Number
2308001066
Analysis Plan