Addressing Economic Constraints of Technology Adoption for Postharvest Practices and Rice Seed Storage in Bangladesh

Last registered on November 22, 2019

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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

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

November 22, 2019, 11:04 AM EST

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

Locations

Country

Bangladesh

Region

Sherpur and Narail

Primary Investigator

Name

Jisang Yu

Affiliation

Kansas State University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Benjamin Schwab

PI Affiliation

Kansas State University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2019-11-27

End date

2020-10-31

Keywords

Agriculture

Additional Keywords

risk, liquidity, technology adoption, willingness-to-pay

JEL code(s)

O13, O33, Q12, Q16

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

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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

We combine the BDM auction with a random assignment of contracts.

Experimental Design Details

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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