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Addressing Economic Constraints of Technology Adoption for Postharvest Practices and Rice Seed Storage in Bangladesh
Last registered on November 22, 2019

Pre-Trial

Trial Information
General Information
Title
Addressing Economic Constraints of Technology Adoption for Postharvest Practices and Rice Seed Storage in Bangladesh
RCT ID
AEARCTR-0005035
Initial registration date
November 21, 2019
Last updated
November 22, 2019 11:04 AM EST
Location(s)

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Primary Investigator
Affiliation
Kansas State University
Other Primary Investigator(s)
PI Affiliation
Kansas State University
Additional Trial Information
Status
In development
Start date
2019-11-27
End date
2020-10-31
Secondary IDs
Abstract
Successful adoption of relatively new post-harvest technologies in developing countries is often linked to the provision of mechanisms that allow potential users to overcome economic constraints. Effective research into designing adoption mechanisms incorporates two key dimensions: understanding the technical and perceived value of technologies by users, and carefully evaluating their implementation. We focus on the mechanism design for the adoption of hermetic bags (i.e. GrainPro bag) for rice seed storage. Proper seed storage may boost crop yields by increasing the germination rates and hermetic storage leads to higher quality seeds. While many economic studies examine the factors that deter technology adoptions in various contexts, we plan to focus on two constraints, liquidity and risk. Our goal is to evaluate which constraints serve as key barriers for smallholders in Bangladesh. Providing evidence on the relative importance of these pathways permits the design of more effective tools to target mechanisms that facilitate the adoption of valued technologies.
External Link(s)
Registration Citation
Citation
Schwab, Benjamin and Jisang Yu. 2019. "Addressing Economic Constraints of Technology Adoption for Postharvest Practices and Rice Seed Storage in Bangladesh." AEA RCT Registry. November 22. https://doi.org/10.1257/rct.5035-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We will conduct an adoption experiment featuring a hermetic storage bag (GrainPro bag) for rice seed storage. We plan to do a Becker-DeGroot-Marschak (BDM) auction to elicit the willingness-to-pay for a hermetic bag during the baseline survey along with the following interventions: T1) a warranty, T2) delayed payment (loan) for the storage bag, and C) control. The treatment will be randomly assigned. We plan to do village-level cluster randomization.
Intervention Start Date
2019-11-27
Intervention End Date
2019-12-31
Primary Outcomes
Primary Outcomes (end points)
Willingness-to-pay for the hermetic bag
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
the use of the hermetic bag; storage and sales decisions of rice seed; rice production (acres, other input uses)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We combine the BDM auction with a random assignment of contracts.
Experimental Design Details
Not available
Randomization Method
Randomization will be done in-office by a statistical software (Stata).
Randomization Unit
We plan to do village-level random assignment of the treatment.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
The number of clusters is 90 (villages). We sample 15 villages from each Upazila, 3 Upazilas from each district. Target districts are Narali and Sherpur.
Sample size: planned number of observations
From each village, we plan to sample 10 farms. Thus, the total planned number of observations is 900 farms. We randomly draw 10 farms from the pool of eligible farms in each village and the eligibility is defined as "the farms with positive Aman rice production but not greater than 300 acres for the last two Aman seasons".
Sample size (or number of clusters) by treatment arms
We plan to assign 30 villages into each treatment arms: 30 villages for T1, 30 villages for T2, 30 villages for C. Thus, each treatment arm will have 300 farms (observations).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Assuming the standard deviation is 10% of the baseline mean, with 30 clusters in each arm and the cluster size of 10, the minimum detectable effect size ranges from 2.43% (with intra cluster correlation of 0.01) to 5.46% (with intra cluster correlation of 0.5). Assuming the standard deviation is 50% of the baseline mean, with 30 clusters in each arm and the cluster size of 10, the minimum detectable effect size ranges from 12.15% (with intra cluster correlation of 0.01) to 27.28% (with intra cluster correlation of 0.5). We use Stata package "clustersampsi" for the calculations.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University Research Compliance Office, Kansas State University
IRB Approval Date
2019-11-06
IRB Approval Number
9879.1 (after the review, the project is exempt from further review)