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Agricultural Risk Management in India
Last registered on April 01, 2015

Pre-Trial

Trial Information
General Information
Title
Agricultural Risk Management in India
RCT ID
AEARCTR-0000677
Initial registration date
April 01, 2015
Last updated
April 01, 2015 10:37 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
HBS
Other Primary Investigator(s)
PI Affiliation
Wharton, University of Pennsylvania
Additional Trial Information
Status
On going
Start date
2006-05-01
End date
2015-12-01
Secondary IDs
Abstract
Many rural poor in the developing world engage in agricultural work, either as cultivators or laborers. These individuals often face serious risks from weather fluctuations – above all, erratic, excess or inadequate rainfall. Households attempt to informally insure themselves against these risks by diversifying the crops that they grow and by relying on friends and family in times of distress. However, informal mechanisms do not work as well when everybody in a given area is affected by the same adverse weather event. Historically, formal yield-based crop insurance has suffered from monitoring, selection, and incentive problems as well as high administrative costs.

Rainfall index insurance is a product that could help farmers hedge against agricultural losses due to excess or deficit rainfall, which is often cited as the largest risk to agricultural households. In a rainfall index product, policies pay out when rainfall exceeds or falls below pre-determined triggers based on historical rainfall data. Payouts are easy to calculate and index-based policies are simple to price since rainfall data are readily available. Another advantage of rainfall insurance is that the transaction costs can be low since insurance companies do not need to verify claims. Further, since farmers have no control over the level of rainfall, their choices to adopt the insurance or modify other behavior do not affect the sustainability of the insurance, in contrast to many yield-based crop insurance programs.

This randomized evaluation will help shed light on decision-making about weather risk and evaluate the potential of rainfall insurance to improve the livelihood and sustainability of rural poor households.
External Link(s)
Registration Citation
Citation
Cole, Shawn and Jeremy Tobacman. 2015. "Agricultural Risk Management in India." AEA RCT Registry. April 01. https://doi.org/10.1257/rct.677-2.0.
Former Citation
Cole, Shawn and Jeremy Tobacman. 2015. "Agricultural Risk Management in India." AEA RCT Registry. April 01. https://www.socialscienceregistry.org/trials/677/history/3950.
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Experimental Details
Interventions
Intervention(s)
Intervention is the index-based rainfall insurance product marketed to treatment households, the product has been marketed and distributed by SEWA, the Self Employed Women’s Association.
Intervention Start Date
2006-05-01
Intervention End Date
2015-06-01
Primary Outcomes
Primary Outcomes (end points)
Insurance purchase, Household level agricultural outcomes (ex-post and ex-ante) i.e. revenues, costs, area cultivated, cash crop production etc., determinants of household demand for rainfall insurance.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The study has been conducting annual household surveys since the beginning of May 2006, collecting data on agricultural production, income, consumption and well-being. In addition to the village-level random variation in access to the insurance, incentives and information relevant for purchase choices are randomized at the individual level.

The first incentive manipulation implemented was a price discount, to estimate the elasticity of demand. In addition, willingness to pay for the rainfall insurance has been elicited repeatedly via the Becker-Degroot-Marschak (BDM) methodology. The random offer prices induce random variation in coverage, supporting the intent-to-treat impact evaluation design. The study utilized paper flyers and handheld video players, among other means, to randomize information relevant to purchase decisions. Variations of both flyers and videos were presented to randomly selected subsets of treatment households. Also, in 2010 a short financial literacy module was conducted, to which invitations were randomized at the household level. The incentive and information manipulations administered in the project are useful as instruments for take-up and also are of independent interest.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Village Level
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
108 Villages (Initial plan was 100 villages, 8 villages added)
Sample size: planned number of observations
2041 Households
Sample size (or number of clusters) by treatment arms
The ongoing study includes 108 villages in the Ahmedabad, Anand and Patan districts of Gujarat. 48 villages belong to the control group, while weather insurance was rolled out to the 60 treatment villages in three waves.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard Committee on the Use of Human Subjects
IRB Approval Date
2006-04-23
IRB Approval Number
F14837
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers