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Impact Evaluation Associated with the Bangladesh Agricultural Value Chains project
Last registered on June 15, 2016


Trial Information
General Information
Impact Evaluation Associated with the Bangladesh Agricultural Value Chains project
Initial registration date
June 15, 2016
Last updated
June 15, 2016 1:23 PM EDT
Primary Investigator
International Food Policy Research Institute
Other Primary Investigator(s)
PI Affiliation
International Food Policy Research Institute
PI Affiliation
International Food Policy Research Institute
Additional Trial Information
On going
Start date
End date
Secondary IDs
In this impact evaluation, we are partnering with the Bangladesh Agricultural Value Chains Project to test interventions within specific value chains for impacts on outcomes among smallholders. The goal of the interventions being studied is to move the value chain from an equilibrium in which farmers both face uncertainty about input quality and crop marketing methods to an equilibrium in which high quality inputs are available and used by smallholders, and they are better able to receive high prices for their output as it is of less variable quality. For the jute value chain, the impact evaluation centers around cluster randomizing both training and promotional events for high quality, certified, and branded fertilizer. The promotional events included a raffle for discounts for fertilizer; main outcomes to be studied include whether or not treated households participated in the trainings or promotional events; use of improved inputs; attitudes about improved inputs and trust in input retailers or input brands; specific crop production and yields; and agricultural revenue and profits.
External Link(s)
Registration Citation
de Brauw, Alan, Berber Kramer and Hazel Malapit. 2016. "Impact Evaluation Associated with the Bangladesh Agricultural Value Chains project." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.1295-1.0.
Former Citation
de Brauw, Alan et al. 2016. "Impact Evaluation Associated with the Bangladesh Agricultural Value Chains project." AEA RCT Registry. June 15. http://www.socialscienceregistry.org/trials/1295/history/8859.
Sponsors & Partners

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Experimental Details
We are working with the Bangladesh Agricultural Value Chains project to pilot test randomized interventions to observe their impacts on productivity of specific crops- jute and mung beans. In the jute value chain, we are specifically studying the impacts of trainings conducted by NGOs and of promotional activities conducted by input dealers to market improved or reliable inputs to smallholders. The interventions are being implemented as a cluster randomized control trial; the two interventions described above were cross-randomized. Mung bean interventions are in the planning stage for the 2017 growing season. More complete descriptions of the interventions are available from the principal investigators.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Key outcomes include participation; input purchases, input use, agricultural productivity, consumption expenditures, and food security.
Primary Outcomes (explanation)
We will use the Women's Empowerment in Agriculture index as an additional outcome; its construction is discussed at the WEAI Resource Center (https://www.ifpri.org/topic/weai-resource-center).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will have similar experimental designs embedded in two value chains, those for jute and mung beans. Both are part of the larger AVC project. In the jute value chain, For jute, villages were first randomized into two groups: 25 villages that will receive training and 25 that will not. Villages were then additionally randomized into promotion and non-promotion groups, yielding the following four treatment groups:

1. Training + Promotion (13 villages)
2. Training + No Promotion (12 villages)
3. No Training + Promotion (13 villages)
4. No Training + No Promotion (12 villages)

Within the promotion villages, a raffle was held for households to receive coupons towards the purchase of specifically branded NPKS fertilizer which is helpful for jute cultivation specifically. The discounts were 80%, 50%, and 20%. The raffle was specifically held among farmers in the promotional groups. Therefore we can calculate statistical power in terms of either participation in one arm of the cluster RCT or in terms of categories of lottery winners.

Experimental Design Details
Randomization Method
Randomization was done by computer and is still to be conducted for the mung bean groups.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
50 clusters for jute value chain, 50 clusters for the mung bean value chain
Sample size: planned number of observations
2000 household level observations (both males and females were interviewed in households when available, so the targeted sample size was actually 4000 interviews)
Sample size (or number of clusters) by treatment arms
See above.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For the jute intervention, we compute the minimum detectable effect for discrete variables generally as a function of the baseline adoption rate. For smaller initial rates, the minimum detectable effect is smaller in terms of percentage points; for example, if there is 10 percent adoption, an 11 percentage point difference is detectable. If the initial adoption rate is higher, a larger percentage point difference is needed to identify outcomes, but the percent change need not be as large. For jute yields, we can identify a 13 percent difference from the control group in any specific arm of the intervention. Among raffle winners, the difference is 23 percent; among 3rd place winners, 16 percent. We are more likely to be able to detect the impacts of winning the raffle or coming in 2nd place because they are more likely to redeem coupons. In general, for a continuous variable the MDE is 0.31 standard deviations.
IRB Name
International Food Policy Research Institute
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)